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Binary Feature Mask Optimization for Feature Selection (2401.12644v3)

Published 23 Jan 2024 in cs.LG

Abstract: We investigate feature selection problem for generic machine learning models. We introduce a novel framework that selects features considering the outcomes of the model. Our framework introduces a novel feature masking approach to eliminate the features during the selection process, instead of completely removing them from the dataset. This allows us to use the same machine learning model during feature selection, unlike other feature selection methods where we need to train the machine learning model again as the dataset has different dimensions on each iteration. We obtain the mask operator using the predictions of the machine learning model, which offers a comprehensive view on the subsets of the features essential for the predictive performance of the model. A variety of approaches exist in the feature selection literature. However, to our knowledge, no study has introduced a training-free framework for a generic machine learning model to select features while considering the importance of the feature subsets as a whole, instead of focusing on the individual features. We demonstrate significant performance improvements on the real-life datasets under different settings using LightGBM and Multi-Layer Perceptron as our machine learning models. The high performance of our General Binary Mask Optimization algorithm stems from its feature masking approach to select features and its flexibility in the number of selected features. The algorithm selects features based on the validation performance of the machine learning model. Hence, the number of selected features is not predetermined and adjusts dynamically to the dataset. Additionally, we openly share the implementation or our code to encourage further research in this area.

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References (18)
  1. Bishop, C. M. Pattern Recognition and Machine Learning (Springer, 2006). [2] Dhal, P. & Azad, C. A comprehensive survey on feature selection in the various fields of machine learning. Applied Intelligence 1–39 (2022). [3] Abdulwahab, H. M., Ajitha, S. & Saif, M. A. N. Feature selection techniques in the context of big data: taxonomy and analysis. Applied Intelligence 52, 13568–13613 (2022). [4] Feofanov, V., Devijver, E. & Amini, M.-R. Wrapper feature selection with partially labeled data. Applied Intelligence 52, 12316–12329 (2022). [5] Zhou, H., Wang, X. & Zhu, R. Feature selection based on mutual information with correlation coefficient. Applied Intelligence 1–18 (2022). [6] Pudjihartono, N., Fadason, T., Kempa-Liehr, A. W. & O’Sullivan, J. M. A review of feature selection methods for machine learning-based disease risk prediction. Frontiers in Bioinformatics 2, 927312 (2022). [7] Guyon, I. & Elisseeff, A. An introduction to variable and feature selection. Journal of machine learning research 3, 1157–1182 (2003). [8] Vinh, L. T., Lee, S., Park, Y.-T. & d’Auriol, B. J. A novel feature selection method based on normalized mutual information. Applied Intelligence 37, 100–120 (2012). [9] Cover, T. & Thomas, J. Elements of Information Theory (Wiley-Interscience, 2006). [10] Giraud, C. Introduction to High-Dimensional Statistics (Chapman and Hall/CRC, 2015). [11] Cawley, G. C. & Talbot, N. L. C. On over-fitting in model selection and subsequent selection bias in performance evaluation. Journal of Machine Learning Research 11, 2079–2107 (2010). URL http://jmlr.org/papers/v11/cawley10a.html. [12] Dua, D. & Graff, C. Uci machine learning repository (2017). URL http://archive.ics.uci.edu/ml. [13] Sejnowski, T. & Gorman, R. Connectionist Bench (Sonar, Mines vs. Rocks). UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5T01Q. [14] Rafiei, M. Residential Building Data Set. UCI Machine Learning Repository (2018). DOI: https://doi.org/10.24432/C5S896. [15] Ke, G. et al. Guyon, I. et al. (eds) Lightgbm: A highly efficient gradient boosting decision tree. (eds Guyon, I. et al.) Advances in Neural Information Processing Systems, Vol. 30 (Curran Associates, Inc., 2017). URL https://proceedings.neurips.cc/paper_files/paper/2017/file/6449f44a102fde848669bdd9eb6b76fa-Paper.pdf. [16] Goodfellow, I. J., Bengio, Y. & Courville, A. Deep Learning (MIT Press, Cambridge, MA, USA, 2016). http://www.deeplearningbook.org. [17] Vergara, J. R. & Estévez, P. A. A review of feature selection methods based on mutual information. Neural computing and applications 24, 175–186 (2014). [18] Farahani, G. Feature selection based on cross-correlation for the intrusion detection system. Security and Communication Networks 1–17 (2020). Dhal, P. & Azad, C. A comprehensive survey on feature selection in the various fields of machine learning. Applied Intelligence 1–39 (2022). [3] Abdulwahab, H. M., Ajitha, S. & Saif, M. A. N. Feature selection techniques in the context of big data: taxonomy and analysis. Applied Intelligence 52, 13568–13613 (2022). [4] Feofanov, V., Devijver, E. & Amini, M.-R. Wrapper feature selection with partially labeled data. Applied Intelligence 52, 12316–12329 (2022). [5] Zhou, H., Wang, X. & Zhu, R. Feature selection based on mutual information with correlation coefficient. Applied Intelligence 1–18 (2022). [6] Pudjihartono, N., Fadason, T., Kempa-Liehr, A. W. & O’Sullivan, J. M. A review of feature selection methods for machine learning-based disease risk prediction. Frontiers in Bioinformatics 2, 927312 (2022). [7] Guyon, I. & Elisseeff, A. An introduction to variable and feature selection. Journal of machine learning research 3, 1157–1182 (2003). [8] Vinh, L. T., Lee, S., Park, Y.-T. & d’Auriol, B. J. A novel feature selection method based on normalized mutual information. Applied Intelligence 37, 100–120 (2012). [9] Cover, T. & Thomas, J. Elements of Information Theory (Wiley-Interscience, 2006). [10] Giraud, C. Introduction to High-Dimensional Statistics (Chapman and Hall/CRC, 2015). [11] Cawley, G. C. & Talbot, N. L. C. On over-fitting in model selection and subsequent selection bias in performance evaluation. Journal of Machine Learning Research 11, 2079–2107 (2010). URL http://jmlr.org/papers/v11/cawley10a.html. [12] Dua, D. & Graff, C. Uci machine learning repository (2017). URL http://archive.ics.uci.edu/ml. [13] Sejnowski, T. & Gorman, R. Connectionist Bench (Sonar, Mines vs. Rocks). UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5T01Q. [14] Rafiei, M. Residential Building Data Set. UCI Machine Learning Repository (2018). DOI: https://doi.org/10.24432/C5S896. [15] Ke, G. et al. Guyon, I. et al. (eds) Lightgbm: A highly efficient gradient boosting decision tree. (eds Guyon, I. et al.) Advances in Neural Information Processing Systems, Vol. 30 (Curran Associates, Inc., 2017). URL https://proceedings.neurips.cc/paper_files/paper/2017/file/6449f44a102fde848669bdd9eb6b76fa-Paper.pdf. [16] Goodfellow, I. J., Bengio, Y. & Courville, A. Deep Learning (MIT Press, Cambridge, MA, USA, 2016). http://www.deeplearningbook.org. [17] Vergara, J. R. & Estévez, P. A. A review of feature selection methods based on mutual information. Neural computing and applications 24, 175–186 (2014). [18] Farahani, G. Feature selection based on cross-correlation for the intrusion detection system. Security and Communication Networks 1–17 (2020). Abdulwahab, H. M., Ajitha, S. & Saif, M. A. N. Feature selection techniques in the context of big data: taxonomy and analysis. Applied Intelligence 52, 13568–13613 (2022). [4] Feofanov, V., Devijver, E. & Amini, M.-R. Wrapper feature selection with partially labeled data. Applied Intelligence 52, 12316–12329 (2022). [5] Zhou, H., Wang, X. & Zhu, R. Feature selection based on mutual information with correlation coefficient. Applied Intelligence 1–18 (2022). [6] Pudjihartono, N., Fadason, T., Kempa-Liehr, A. W. & O’Sullivan, J. M. A review of feature selection methods for machine learning-based disease risk prediction. Frontiers in Bioinformatics 2, 927312 (2022). [7] Guyon, I. & Elisseeff, A. An introduction to variable and feature selection. Journal of machine learning research 3, 1157–1182 (2003). [8] Vinh, L. T., Lee, S., Park, Y.-T. & d’Auriol, B. J. A novel feature selection method based on normalized mutual information. Applied Intelligence 37, 100–120 (2012). [9] Cover, T. & Thomas, J. Elements of Information Theory (Wiley-Interscience, 2006). [10] Giraud, C. Introduction to High-Dimensional Statistics (Chapman and Hall/CRC, 2015). [11] Cawley, G. C. & Talbot, N. L. C. On over-fitting in model selection and subsequent selection bias in performance evaluation. Journal of Machine Learning Research 11, 2079–2107 (2010). URL http://jmlr.org/papers/v11/cawley10a.html. [12] Dua, D. & Graff, C. Uci machine learning repository (2017). URL http://archive.ics.uci.edu/ml. [13] Sejnowski, T. & Gorman, R. Connectionist Bench (Sonar, Mines vs. Rocks). UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5T01Q. [14] Rafiei, M. Residential Building Data Set. UCI Machine Learning Repository (2018). DOI: https://doi.org/10.24432/C5S896. [15] Ke, G. et al. Guyon, I. et al. (eds) Lightgbm: A highly efficient gradient boosting decision tree. (eds Guyon, I. et al.) Advances in Neural Information Processing Systems, Vol. 30 (Curran Associates, Inc., 2017). URL https://proceedings.neurips.cc/paper_files/paper/2017/file/6449f44a102fde848669bdd9eb6b76fa-Paper.pdf. [16] Goodfellow, I. J., Bengio, Y. & Courville, A. Deep Learning (MIT Press, Cambridge, MA, USA, 2016). http://www.deeplearningbook.org. [17] Vergara, J. R. & Estévez, P. A. A review of feature selection methods based on mutual information. Neural computing and applications 24, 175–186 (2014). [18] Farahani, G. Feature selection based on cross-correlation for the intrusion detection system. Security and Communication Networks 1–17 (2020). Feofanov, V., Devijver, E. & Amini, M.-R. Wrapper feature selection with partially labeled data. Applied Intelligence 52, 12316–12329 (2022). [5] Zhou, H., Wang, X. & Zhu, R. Feature selection based on mutual information with correlation coefficient. Applied Intelligence 1–18 (2022). [6] Pudjihartono, N., Fadason, T., Kempa-Liehr, A. W. & O’Sullivan, J. M. A review of feature selection methods for machine learning-based disease risk prediction. Frontiers in Bioinformatics 2, 927312 (2022). [7] Guyon, I. & Elisseeff, A. An introduction to variable and feature selection. Journal of machine learning research 3, 1157–1182 (2003). [8] Vinh, L. T., Lee, S., Park, Y.-T. & d’Auriol, B. J. A novel feature selection method based on normalized mutual information. Applied Intelligence 37, 100–120 (2012). [9] Cover, T. & Thomas, J. Elements of Information Theory (Wiley-Interscience, 2006). [10] Giraud, C. Introduction to High-Dimensional Statistics (Chapman and Hall/CRC, 2015). [11] Cawley, G. C. & Talbot, N. L. C. On over-fitting in model selection and subsequent selection bias in performance evaluation. Journal of Machine Learning Research 11, 2079–2107 (2010). URL http://jmlr.org/papers/v11/cawley10a.html. [12] Dua, D. & Graff, C. Uci machine learning repository (2017). URL http://archive.ics.uci.edu/ml. [13] Sejnowski, T. & Gorman, R. Connectionist Bench (Sonar, Mines vs. Rocks). UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5T01Q. [14] Rafiei, M. Residential Building Data Set. UCI Machine Learning Repository (2018). DOI: https://doi.org/10.24432/C5S896. [15] Ke, G. et al. Guyon, I. et al. (eds) Lightgbm: A highly efficient gradient boosting decision tree. (eds Guyon, I. et al.) Advances in Neural Information Processing Systems, Vol. 30 (Curran Associates, Inc., 2017). URL https://proceedings.neurips.cc/paper_files/paper/2017/file/6449f44a102fde848669bdd9eb6b76fa-Paper.pdf. [16] Goodfellow, I. J., Bengio, Y. & Courville, A. Deep Learning (MIT Press, Cambridge, MA, USA, 2016). http://www.deeplearningbook.org. [17] Vergara, J. R. & Estévez, P. A. A review of feature selection methods based on mutual information. Neural computing and applications 24, 175–186 (2014). [18] Farahani, G. Feature selection based on cross-correlation for the intrusion detection system. Security and Communication Networks 1–17 (2020). Zhou, H., Wang, X. & Zhu, R. Feature selection based on mutual information with correlation coefficient. Applied Intelligence 1–18 (2022). [6] Pudjihartono, N., Fadason, T., Kempa-Liehr, A. W. & O’Sullivan, J. M. A review of feature selection methods for machine learning-based disease risk prediction. Frontiers in Bioinformatics 2, 927312 (2022). [7] Guyon, I. & Elisseeff, A. An introduction to variable and feature selection. Journal of machine learning research 3, 1157–1182 (2003). [8] Vinh, L. T., Lee, S., Park, Y.-T. & d’Auriol, B. J. A novel feature selection method based on normalized mutual information. Applied Intelligence 37, 100–120 (2012). [9] Cover, T. & Thomas, J. Elements of Information Theory (Wiley-Interscience, 2006). [10] Giraud, C. Introduction to High-Dimensional Statistics (Chapman and Hall/CRC, 2015). [11] Cawley, G. C. & Talbot, N. L. C. On over-fitting in model selection and subsequent selection bias in performance evaluation. Journal of Machine Learning Research 11, 2079–2107 (2010). URL http://jmlr.org/papers/v11/cawley10a.html. [12] Dua, D. & Graff, C. Uci machine learning repository (2017). URL http://archive.ics.uci.edu/ml. [13] Sejnowski, T. & Gorman, R. Connectionist Bench (Sonar, Mines vs. Rocks). UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5T01Q. [14] Rafiei, M. Residential Building Data Set. UCI Machine Learning Repository (2018). DOI: https://doi.org/10.24432/C5S896. [15] Ke, G. et al. Guyon, I. et al. (eds) Lightgbm: A highly efficient gradient boosting decision tree. (eds Guyon, I. et al.) Advances in Neural Information Processing Systems, Vol. 30 (Curran Associates, Inc., 2017). URL https://proceedings.neurips.cc/paper_files/paper/2017/file/6449f44a102fde848669bdd9eb6b76fa-Paper.pdf. [16] Goodfellow, I. J., Bengio, Y. & Courville, A. Deep Learning (MIT Press, Cambridge, MA, USA, 2016). http://www.deeplearningbook.org. [17] Vergara, J. R. & Estévez, P. A. A review of feature selection methods based on mutual information. Neural computing and applications 24, 175–186 (2014). [18] Farahani, G. Feature selection based on cross-correlation for the intrusion detection system. Security and Communication Networks 1–17 (2020). Pudjihartono, N., Fadason, T., Kempa-Liehr, A. W. & O’Sullivan, J. M. A review of feature selection methods for machine learning-based disease risk prediction. Frontiers in Bioinformatics 2, 927312 (2022). [7] Guyon, I. & Elisseeff, A. An introduction to variable and feature selection. Journal of machine learning research 3, 1157–1182 (2003). [8] Vinh, L. T., Lee, S., Park, Y.-T. & d’Auriol, B. J. A novel feature selection method based on normalized mutual information. Applied Intelligence 37, 100–120 (2012). [9] Cover, T. & Thomas, J. Elements of Information Theory (Wiley-Interscience, 2006). [10] Giraud, C. Introduction to High-Dimensional Statistics (Chapman and Hall/CRC, 2015). [11] Cawley, G. C. & Talbot, N. L. C. On over-fitting in model selection and subsequent selection bias in performance evaluation. Journal of Machine Learning Research 11, 2079–2107 (2010). URL http://jmlr.org/papers/v11/cawley10a.html. [12] Dua, D. & Graff, C. Uci machine learning repository (2017). URL http://archive.ics.uci.edu/ml. [13] Sejnowski, T. & Gorman, R. Connectionist Bench (Sonar, Mines vs. Rocks). UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5T01Q. [14] Rafiei, M. Residential Building Data Set. UCI Machine Learning Repository (2018). DOI: https://doi.org/10.24432/C5S896. [15] Ke, G. et al. Guyon, I. et al. (eds) Lightgbm: A highly efficient gradient boosting decision tree. (eds Guyon, I. et al.) Advances in Neural Information Processing Systems, Vol. 30 (Curran Associates, Inc., 2017). URL https://proceedings.neurips.cc/paper_files/paper/2017/file/6449f44a102fde848669bdd9eb6b76fa-Paper.pdf. [16] Goodfellow, I. J., Bengio, Y. & Courville, A. Deep Learning (MIT Press, Cambridge, MA, USA, 2016). http://www.deeplearningbook.org. [17] Vergara, J. R. & Estévez, P. A. A review of feature selection methods based on mutual information. Neural computing and applications 24, 175–186 (2014). [18] Farahani, G. Feature selection based on cross-correlation for the intrusion detection system. Security and Communication Networks 1–17 (2020). Guyon, I. & Elisseeff, A. An introduction to variable and feature selection. Journal of machine learning research 3, 1157–1182 (2003). [8] Vinh, L. T., Lee, S., Park, Y.-T. & d’Auriol, B. J. A novel feature selection method based on normalized mutual information. Applied Intelligence 37, 100–120 (2012). [9] Cover, T. & Thomas, J. Elements of Information Theory (Wiley-Interscience, 2006). [10] Giraud, C. Introduction to High-Dimensional Statistics (Chapman and Hall/CRC, 2015). [11] Cawley, G. C. & Talbot, N. L. C. On over-fitting in model selection and subsequent selection bias in performance evaluation. Journal of Machine Learning Research 11, 2079–2107 (2010). URL http://jmlr.org/papers/v11/cawley10a.html. [12] Dua, D. & Graff, C. Uci machine learning repository (2017). URL http://archive.ics.uci.edu/ml. [13] Sejnowski, T. & Gorman, R. Connectionist Bench (Sonar, Mines vs. Rocks). UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5T01Q. [14] Rafiei, M. Residential Building Data Set. UCI Machine Learning Repository (2018). DOI: https://doi.org/10.24432/C5S896. [15] Ke, G. et al. Guyon, I. et al. (eds) Lightgbm: A highly efficient gradient boosting decision tree. (eds Guyon, I. et al.) Advances in Neural Information Processing Systems, Vol. 30 (Curran Associates, Inc., 2017). URL https://proceedings.neurips.cc/paper_files/paper/2017/file/6449f44a102fde848669bdd9eb6b76fa-Paper.pdf. [16] Goodfellow, I. J., Bengio, Y. & Courville, A. Deep Learning (MIT Press, Cambridge, MA, USA, 2016). http://www.deeplearningbook.org. [17] Vergara, J. R. & Estévez, P. A. A review of feature selection methods based on mutual information. Neural computing and applications 24, 175–186 (2014). [18] Farahani, G. Feature selection based on cross-correlation for the intrusion detection system. Security and Communication Networks 1–17 (2020). Vinh, L. T., Lee, S., Park, Y.-T. & d’Auriol, B. J. A novel feature selection method based on normalized mutual information. Applied Intelligence 37, 100–120 (2012). [9] Cover, T. & Thomas, J. Elements of Information Theory (Wiley-Interscience, 2006). [10] Giraud, C. Introduction to High-Dimensional Statistics (Chapman and Hall/CRC, 2015). [11] Cawley, G. C. & Talbot, N. L. C. On over-fitting in model selection and subsequent selection bias in performance evaluation. Journal of Machine Learning Research 11, 2079–2107 (2010). URL http://jmlr.org/papers/v11/cawley10a.html. [12] Dua, D. & Graff, C. Uci machine learning repository (2017). URL http://archive.ics.uci.edu/ml. [13] Sejnowski, T. & Gorman, R. Connectionist Bench (Sonar, Mines vs. Rocks). UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5T01Q. [14] Rafiei, M. Residential Building Data Set. UCI Machine Learning Repository (2018). DOI: https://doi.org/10.24432/C5S896. [15] Ke, G. et al. Guyon, I. et al. (eds) Lightgbm: A highly efficient gradient boosting decision tree. (eds Guyon, I. et al.) Advances in Neural Information Processing Systems, Vol. 30 (Curran Associates, Inc., 2017). URL https://proceedings.neurips.cc/paper_files/paper/2017/file/6449f44a102fde848669bdd9eb6b76fa-Paper.pdf. [16] Goodfellow, I. J., Bengio, Y. & Courville, A. Deep Learning (MIT Press, Cambridge, MA, USA, 2016). http://www.deeplearningbook.org. [17] Vergara, J. R. & Estévez, P. A. A review of feature selection methods based on mutual information. Neural computing and applications 24, 175–186 (2014). [18] Farahani, G. Feature selection based on cross-correlation for the intrusion detection system. Security and Communication Networks 1–17 (2020). Cover, T. & Thomas, J. Elements of Information Theory (Wiley-Interscience, 2006). [10] Giraud, C. Introduction to High-Dimensional Statistics (Chapman and Hall/CRC, 2015). [11] Cawley, G. C. & Talbot, N. L. C. On over-fitting in model selection and subsequent selection bias in performance evaluation. Journal of Machine Learning Research 11, 2079–2107 (2010). URL http://jmlr.org/papers/v11/cawley10a.html. [12] Dua, D. & Graff, C. Uci machine learning repository (2017). URL http://archive.ics.uci.edu/ml. [13] Sejnowski, T. & Gorman, R. Connectionist Bench (Sonar, Mines vs. Rocks). UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5T01Q. [14] Rafiei, M. Residential Building Data Set. UCI Machine Learning Repository (2018). DOI: https://doi.org/10.24432/C5S896. [15] Ke, G. et al. Guyon, I. et al. (eds) Lightgbm: A highly efficient gradient boosting decision tree. (eds Guyon, I. et al.) Advances in Neural Information Processing Systems, Vol. 30 (Curran Associates, Inc., 2017). URL https://proceedings.neurips.cc/paper_files/paper/2017/file/6449f44a102fde848669bdd9eb6b76fa-Paper.pdf. [16] Goodfellow, I. J., Bengio, Y. & Courville, A. Deep Learning (MIT Press, Cambridge, MA, USA, 2016). http://www.deeplearningbook.org. [17] Vergara, J. R. & Estévez, P. A. A review of feature selection methods based on mutual information. Neural computing and applications 24, 175–186 (2014). [18] Farahani, G. Feature selection based on cross-correlation for the intrusion detection system. Security and Communication Networks 1–17 (2020). Giraud, C. Introduction to High-Dimensional Statistics (Chapman and Hall/CRC, 2015). [11] Cawley, G. C. & Talbot, N. L. C. On over-fitting in model selection and subsequent selection bias in performance evaluation. Journal of Machine Learning Research 11, 2079–2107 (2010). URL http://jmlr.org/papers/v11/cawley10a.html. [12] Dua, D. & Graff, C. Uci machine learning repository (2017). URL http://archive.ics.uci.edu/ml. [13] Sejnowski, T. & Gorman, R. Connectionist Bench (Sonar, Mines vs. Rocks). UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5T01Q. [14] Rafiei, M. Residential Building Data Set. UCI Machine Learning Repository (2018). DOI: https://doi.org/10.24432/C5S896. [15] Ke, G. et al. Guyon, I. et al. (eds) Lightgbm: A highly efficient gradient boosting decision tree. (eds Guyon, I. et al.) Advances in Neural Information Processing Systems, Vol. 30 (Curran Associates, Inc., 2017). URL https://proceedings.neurips.cc/paper_files/paper/2017/file/6449f44a102fde848669bdd9eb6b76fa-Paper.pdf. [16] Goodfellow, I. J., Bengio, Y. & Courville, A. Deep Learning (MIT Press, Cambridge, MA, USA, 2016). http://www.deeplearningbook.org. [17] Vergara, J. R. & Estévez, P. A. A review of feature selection methods based on mutual information. Neural computing and applications 24, 175–186 (2014). [18] Farahani, G. Feature selection based on cross-correlation for the intrusion detection system. Security and Communication Networks 1–17 (2020). Cawley, G. C. & Talbot, N. L. C. On over-fitting in model selection and subsequent selection bias in performance evaluation. Journal of Machine Learning Research 11, 2079–2107 (2010). URL http://jmlr.org/papers/v11/cawley10a.html. [12] Dua, D. & Graff, C. Uci machine learning repository (2017). URL http://archive.ics.uci.edu/ml. [13] Sejnowski, T. & Gorman, R. Connectionist Bench (Sonar, Mines vs. Rocks). UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5T01Q. [14] Rafiei, M. Residential Building Data Set. UCI Machine Learning Repository (2018). DOI: https://doi.org/10.24432/C5S896. [15] Ke, G. et al. Guyon, I. et al. (eds) Lightgbm: A highly efficient gradient boosting decision tree. (eds Guyon, I. et al.) Advances in Neural Information Processing Systems, Vol. 30 (Curran Associates, Inc., 2017). URL https://proceedings.neurips.cc/paper_files/paper/2017/file/6449f44a102fde848669bdd9eb6b76fa-Paper.pdf. [16] Goodfellow, I. J., Bengio, Y. & Courville, A. Deep Learning (MIT Press, Cambridge, MA, USA, 2016). http://www.deeplearningbook.org. [17] Vergara, J. R. & Estévez, P. A. A review of feature selection methods based on mutual information. Neural computing and applications 24, 175–186 (2014). [18] Farahani, G. Feature selection based on cross-correlation for the intrusion detection system. Security and Communication Networks 1–17 (2020). Dua, D. & Graff, C. Uci machine learning repository (2017). URL http://archive.ics.uci.edu/ml. [13] Sejnowski, T. & Gorman, R. Connectionist Bench (Sonar, Mines vs. Rocks). UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5T01Q. [14] Rafiei, M. Residential Building Data Set. UCI Machine Learning Repository (2018). DOI: https://doi.org/10.24432/C5S896. [15] Ke, G. et al. Guyon, I. et al. (eds) Lightgbm: A highly efficient gradient boosting decision tree. (eds Guyon, I. et al.) Advances in Neural Information Processing Systems, Vol. 30 (Curran Associates, Inc., 2017). URL https://proceedings.neurips.cc/paper_files/paper/2017/file/6449f44a102fde848669bdd9eb6b76fa-Paper.pdf. [16] Goodfellow, I. J., Bengio, Y. & Courville, A. Deep Learning (MIT Press, Cambridge, MA, USA, 2016). http://www.deeplearningbook.org. [17] Vergara, J. R. & Estévez, P. A. A review of feature selection methods based on mutual information. Neural computing and applications 24, 175–186 (2014). [18] Farahani, G. Feature selection based on cross-correlation for the intrusion detection system. Security and Communication Networks 1–17 (2020). Sejnowski, T. & Gorman, R. Connectionist Bench (Sonar, Mines vs. Rocks). UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5T01Q. [14] Rafiei, M. Residential Building Data Set. UCI Machine Learning Repository (2018). DOI: https://doi.org/10.24432/C5S896. [15] Ke, G. et al. Guyon, I. et al. (eds) Lightgbm: A highly efficient gradient boosting decision tree. (eds Guyon, I. et al.) Advances in Neural Information Processing Systems, Vol. 30 (Curran Associates, Inc., 2017). URL https://proceedings.neurips.cc/paper_files/paper/2017/file/6449f44a102fde848669bdd9eb6b76fa-Paper.pdf. [16] Goodfellow, I. J., Bengio, Y. & Courville, A. 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  2. A comprehensive survey on feature selection in the various fields of machine learning. Applied Intelligence 1–39 (2022). [3] Abdulwahab, H. M., Ajitha, S. & Saif, M. A. N. Feature selection techniques in the context of big data: taxonomy and analysis. Applied Intelligence 52, 13568–13613 (2022). [4] Feofanov, V., Devijver, E. & Amini, M.-R. Wrapper feature selection with partially labeled data. Applied Intelligence 52, 12316–12329 (2022). [5] Zhou, H., Wang, X. & Zhu, R. Feature selection based on mutual information with correlation coefficient. Applied Intelligence 1–18 (2022). [6] Pudjihartono, N., Fadason, T., Kempa-Liehr, A. W. & O’Sullivan, J. M. A review of feature selection methods for machine learning-based disease risk prediction. Frontiers in Bioinformatics 2, 927312 (2022). [7] Guyon, I. & Elisseeff, A. An introduction to variable and feature selection. Journal of machine learning research 3, 1157–1182 (2003). [8] Vinh, L. T., Lee, S., Park, Y.-T. & d’Auriol, B. J. 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(eds) Lightgbm: A highly efficient gradient boosting decision tree. (eds Guyon, I. et al.) Advances in Neural Information Processing Systems, Vol. 30 (Curran Associates, Inc., 2017). URL https://proceedings.neurips.cc/paper_files/paper/2017/file/6449f44a102fde848669bdd9eb6b76fa-Paper.pdf. [16] Goodfellow, I. J., Bengio, Y. & Courville, A. Deep Learning (MIT Press, Cambridge, MA, USA, 2016). http://www.deeplearningbook.org. [17] Vergara, J. R. & Estévez, P. A. A review of feature selection methods based on mutual information. Neural computing and applications 24, 175–186 (2014). [18] Farahani, G. Feature selection based on cross-correlation for the intrusion detection system. Security and Communication Networks 1–17 (2020). Abdulwahab, H. M., Ajitha, S. & Saif, M. A. N. Feature selection techniques in the context of big data: taxonomy and analysis. Applied Intelligence 52, 13568–13613 (2022). [4] Feofanov, V., Devijver, E. & Amini, M.-R. Wrapper feature selection with partially labeled data. Applied Intelligence 52, 12316–12329 (2022). [5] Zhou, H., Wang, X. & Zhu, R. Feature selection based on mutual information with correlation coefficient. Applied Intelligence 1–18 (2022). [6] Pudjihartono, N., Fadason, T., Kempa-Liehr, A. W. & O’Sullivan, J. M. A review of feature selection methods for machine learning-based disease risk prediction. Frontiers in Bioinformatics 2, 927312 (2022). [7] Guyon, I. & Elisseeff, A. An introduction to variable and feature selection. Journal of machine learning research 3, 1157–1182 (2003). [8] Vinh, L. T., Lee, S., Park, Y.-T. & d’Auriol, B. J. A novel feature selection method based on normalized mutual information. Applied Intelligence 37, 100–120 (2012). [9] Cover, T. & Thomas, J. Elements of Information Theory (Wiley-Interscience, 2006). [10] Giraud, C. Introduction to High-Dimensional Statistics (Chapman and Hall/CRC, 2015). [11] Cawley, G. C. & Talbot, N. L. C. On over-fitting in model selection and subsequent selection bias in performance evaluation. Journal of Machine Learning Research 11, 2079–2107 (2010). URL http://jmlr.org/papers/v11/cawley10a.html. [12] Dua, D. & Graff, C. Uci machine learning repository (2017). URL http://archive.ics.uci.edu/ml. [13] Sejnowski, T. & Gorman, R. Connectionist Bench (Sonar, Mines vs. Rocks). UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5T01Q. [14] Rafiei, M. Residential Building Data Set. UCI Machine Learning Repository (2018). DOI: https://doi.org/10.24432/C5S896. [15] Ke, G. et al. Guyon, I. et al. (eds) Lightgbm: A highly efficient gradient boosting decision tree. (eds Guyon, I. et al.) Advances in Neural Information Processing Systems, Vol. 30 (Curran Associates, Inc., 2017). URL https://proceedings.neurips.cc/paper_files/paper/2017/file/6449f44a102fde848669bdd9eb6b76fa-Paper.pdf. [16] Goodfellow, I. J., Bengio, Y. & Courville, A. Deep Learning (MIT Press, Cambridge, MA, USA, 2016). http://www.deeplearningbook.org. [17] Vergara, J. R. & Estévez, P. A. A review of feature selection methods based on mutual information. Neural computing and applications 24, 175–186 (2014). [18] Farahani, G. Feature selection based on cross-correlation for the intrusion detection system. Security and Communication Networks 1–17 (2020). Feofanov, V., Devijver, E. & Amini, M.-R. Wrapper feature selection with partially labeled data. Applied Intelligence 52, 12316–12329 (2022). [5] Zhou, H., Wang, X. & Zhu, R. Feature selection based on mutual information with correlation coefficient. Applied Intelligence 1–18 (2022). [6] Pudjihartono, N., Fadason, T., Kempa-Liehr, A. W. & O’Sullivan, J. M. A review of feature selection methods for machine learning-based disease risk prediction. Frontiers in Bioinformatics 2, 927312 (2022). [7] Guyon, I. & Elisseeff, A. An introduction to variable and feature selection. Journal of machine learning research 3, 1157–1182 (2003). [8] Vinh, L. T., Lee, S., Park, Y.-T. & d’Auriol, B. J. A novel feature selection method based on normalized mutual information. Applied Intelligence 37, 100–120 (2012). [9] Cover, T. & Thomas, J. Elements of Information Theory (Wiley-Interscience, 2006). [10] Giraud, C. Introduction to High-Dimensional Statistics (Chapman and Hall/CRC, 2015). [11] Cawley, G. C. & Talbot, N. L. C. On over-fitting in model selection and subsequent selection bias in performance evaluation. Journal of Machine Learning Research 11, 2079–2107 (2010). URL http://jmlr.org/papers/v11/cawley10a.html. [12] Dua, D. & Graff, C. Uci machine learning repository (2017). URL http://archive.ics.uci.edu/ml. [13] Sejnowski, T. & Gorman, R. Connectionist Bench (Sonar, Mines vs. Rocks). UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5T01Q. [14] Rafiei, M. Residential Building Data Set. UCI Machine Learning Repository (2018). DOI: https://doi.org/10.24432/C5S896. [15] Ke, G. et al. Guyon, I. et al. (eds) Lightgbm: A highly efficient gradient boosting decision tree. (eds Guyon, I. et al.) Advances in Neural Information Processing Systems, Vol. 30 (Curran Associates, Inc., 2017). URL https://proceedings.neurips.cc/paper_files/paper/2017/file/6449f44a102fde848669bdd9eb6b76fa-Paper.pdf. [16] Goodfellow, I. J., Bengio, Y. & Courville, A. Deep Learning (MIT Press, Cambridge, MA, USA, 2016). http://www.deeplearningbook.org. [17] Vergara, J. R. & Estévez, P. A. A review of feature selection methods based on mutual information. Neural computing and applications 24, 175–186 (2014). [18] Farahani, G. Feature selection based on cross-correlation for the intrusion detection system. Security and Communication Networks 1–17 (2020). Zhou, H., Wang, X. & Zhu, R. Feature selection based on mutual information with correlation coefficient. Applied Intelligence 1–18 (2022). [6] Pudjihartono, N., Fadason, T., Kempa-Liehr, A. W. & O’Sullivan, J. M. A review of feature selection methods for machine learning-based disease risk prediction. Frontiers in Bioinformatics 2, 927312 (2022). [7] Guyon, I. & Elisseeff, A. An introduction to variable and feature selection. Journal of machine learning research 3, 1157–1182 (2003). [8] Vinh, L. T., Lee, S., Park, Y.-T. & d’Auriol, B. J. A novel feature selection method based on normalized mutual information. Applied Intelligence 37, 100–120 (2012). [9] Cover, T. & Thomas, J. Elements of Information Theory (Wiley-Interscience, 2006). [10] Giraud, C. Introduction to High-Dimensional Statistics (Chapman and Hall/CRC, 2015). [11] Cawley, G. C. & Talbot, N. L. C. On over-fitting in model selection and subsequent selection bias in performance evaluation. Journal of Machine Learning Research 11, 2079–2107 (2010). URL http://jmlr.org/papers/v11/cawley10a.html. [12] Dua, D. & Graff, C. Uci machine learning repository (2017). URL http://archive.ics.uci.edu/ml. [13] Sejnowski, T. & Gorman, R. Connectionist Bench (Sonar, Mines vs. Rocks). UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5T01Q. [14] Rafiei, M. Residential Building Data Set. UCI Machine Learning Repository (2018). DOI: https://doi.org/10.24432/C5S896. [15] Ke, G. et al. Guyon, I. et al. (eds) Lightgbm: A highly efficient gradient boosting decision tree. (eds Guyon, I. et al.) Advances in Neural Information Processing Systems, Vol. 30 (Curran Associates, Inc., 2017). URL https://proceedings.neurips.cc/paper_files/paper/2017/file/6449f44a102fde848669bdd9eb6b76fa-Paper.pdf. [16] Goodfellow, I. J., Bengio, Y. & Courville, A. Deep Learning (MIT Press, Cambridge, MA, USA, 2016). http://www.deeplearningbook.org. [17] Vergara, J. R. & Estévez, P. A. A review of feature selection methods based on mutual information. Neural computing and applications 24, 175–186 (2014). [18] Farahani, G. Feature selection based on cross-correlation for the intrusion detection system. Security and Communication Networks 1–17 (2020). Pudjihartono, N., Fadason, T., Kempa-Liehr, A. W. & O’Sullivan, J. M. A review of feature selection methods for machine learning-based disease risk prediction. Frontiers in Bioinformatics 2, 927312 (2022). [7] Guyon, I. & Elisseeff, A. An introduction to variable and feature selection. Journal of machine learning research 3, 1157–1182 (2003). [8] Vinh, L. T., Lee, S., Park, Y.-T. & d’Auriol, B. J. A novel feature selection method based on normalized mutual information. Applied Intelligence 37, 100–120 (2012). [9] Cover, T. & Thomas, J. Elements of Information Theory (Wiley-Interscience, 2006). [10] Giraud, C. Introduction to High-Dimensional Statistics (Chapman and Hall/CRC, 2015). [11] Cawley, G. C. & Talbot, N. L. C. On over-fitting in model selection and subsequent selection bias in performance evaluation. Journal of Machine Learning Research 11, 2079–2107 (2010). URL http://jmlr.org/papers/v11/cawley10a.html. [12] Dua, D. & Graff, C. Uci machine learning repository (2017). URL http://archive.ics.uci.edu/ml. [13] Sejnowski, T. & Gorman, R. Connectionist Bench (Sonar, Mines vs. Rocks). UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5T01Q. [14] Rafiei, M. Residential Building Data Set. UCI Machine Learning Repository (2018). DOI: https://doi.org/10.24432/C5S896. [15] Ke, G. et al. Guyon, I. et al. (eds) Lightgbm: A highly efficient gradient boosting decision tree. (eds Guyon, I. et al.) Advances in Neural Information Processing Systems, Vol. 30 (Curran Associates, Inc., 2017). URL https://proceedings.neurips.cc/paper_files/paper/2017/file/6449f44a102fde848669bdd9eb6b76fa-Paper.pdf. [16] Goodfellow, I. J., Bengio, Y. & Courville, A. Deep Learning (MIT Press, Cambridge, MA, USA, 2016). http://www.deeplearningbook.org. [17] Vergara, J. R. & Estévez, P. A. A review of feature selection methods based on mutual information. Neural computing and applications 24, 175–186 (2014). [18] Farahani, G. Feature selection based on cross-correlation for the intrusion detection system. Security and Communication Networks 1–17 (2020). Guyon, I. & Elisseeff, A. An introduction to variable and feature selection. Journal of machine learning research 3, 1157–1182 (2003). [8] Vinh, L. T., Lee, S., Park, Y.-T. & d’Auriol, B. J. A novel feature selection method based on normalized mutual information. Applied Intelligence 37, 100–120 (2012). [9] Cover, T. & Thomas, J. Elements of Information Theory (Wiley-Interscience, 2006). [10] Giraud, C. Introduction to High-Dimensional Statistics (Chapman and Hall/CRC, 2015). [11] Cawley, G. C. & Talbot, N. L. C. On over-fitting in model selection and subsequent selection bias in performance evaluation. Journal of Machine Learning Research 11, 2079–2107 (2010). URL http://jmlr.org/papers/v11/cawley10a.html. [12] Dua, D. & Graff, C. Uci machine learning repository (2017). URL http://archive.ics.uci.edu/ml. [13] Sejnowski, T. & Gorman, R. Connectionist Bench (Sonar, Mines vs. Rocks). UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5T01Q. [14] Rafiei, M. Residential Building Data Set. UCI Machine Learning Repository (2018). DOI: https://doi.org/10.24432/C5S896. [15] Ke, G. et al. Guyon, I. et al. (eds) Lightgbm: A highly efficient gradient boosting decision tree. (eds Guyon, I. et al.) Advances in Neural Information Processing Systems, Vol. 30 (Curran Associates, Inc., 2017). URL https://proceedings.neurips.cc/paper_files/paper/2017/file/6449f44a102fde848669bdd9eb6b76fa-Paper.pdf. [16] Goodfellow, I. J., Bengio, Y. & Courville, A. Deep Learning (MIT Press, Cambridge, MA, USA, 2016). http://www.deeplearningbook.org. [17] Vergara, J. R. & Estévez, P. A. A review of feature selection methods based on mutual information. Neural computing and applications 24, 175–186 (2014). [18] Farahani, G. Feature selection based on cross-correlation for the intrusion detection system. Security and Communication Networks 1–17 (2020). Vinh, L. T., Lee, S., Park, Y.-T. & d’Auriol, B. J. A novel feature selection method based on normalized mutual information. Applied Intelligence 37, 100–120 (2012). [9] Cover, T. & Thomas, J. Elements of Information Theory (Wiley-Interscience, 2006). [10] Giraud, C. Introduction to High-Dimensional Statistics (Chapman and Hall/CRC, 2015). [11] Cawley, G. C. & Talbot, N. L. C. On over-fitting in model selection and subsequent selection bias in performance evaluation. Journal of Machine Learning Research 11, 2079–2107 (2010). URL http://jmlr.org/papers/v11/cawley10a.html. [12] Dua, D. & Graff, C. Uci machine learning repository (2017). URL http://archive.ics.uci.edu/ml. [13] Sejnowski, T. & Gorman, R. Connectionist Bench (Sonar, Mines vs. Rocks). UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5T01Q. [14] Rafiei, M. Residential Building Data Set. UCI Machine Learning Repository (2018). DOI: https://doi.org/10.24432/C5S896. [15] Ke, G. et al. Guyon, I. et al. (eds) Lightgbm: A highly efficient gradient boosting decision tree. (eds Guyon, I. et al.) Advances in Neural Information Processing Systems, Vol. 30 (Curran Associates, Inc., 2017). URL https://proceedings.neurips.cc/paper_files/paper/2017/file/6449f44a102fde848669bdd9eb6b76fa-Paper.pdf. [16] Goodfellow, I. J., Bengio, Y. & Courville, A. Deep Learning (MIT Press, Cambridge, MA, USA, 2016). http://www.deeplearningbook.org. [17] Vergara, J. R. & Estévez, P. A. A review of feature selection methods based on mutual information. Neural computing and applications 24, 175–186 (2014). [18] Farahani, G. Feature selection based on cross-correlation for the intrusion detection system. Security and Communication Networks 1–17 (2020). Cover, T. & Thomas, J. Elements of Information Theory (Wiley-Interscience, 2006). [10] Giraud, C. Introduction to High-Dimensional Statistics (Chapman and Hall/CRC, 2015). [11] Cawley, G. C. & Talbot, N. L. C. On over-fitting in model selection and subsequent selection bias in performance evaluation. Journal of Machine Learning Research 11, 2079–2107 (2010). URL http://jmlr.org/papers/v11/cawley10a.html. [12] Dua, D. & Graff, C. Uci machine learning repository (2017). URL http://archive.ics.uci.edu/ml. [13] Sejnowski, T. & Gorman, R. Connectionist Bench (Sonar, Mines vs. Rocks). UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5T01Q. [14] Rafiei, M. Residential Building Data Set. UCI Machine Learning Repository (2018). DOI: https://doi.org/10.24432/C5S896. [15] Ke, G. et al. Guyon, I. et al. (eds) Lightgbm: A highly efficient gradient boosting decision tree. (eds Guyon, I. et al.) Advances in Neural Information Processing Systems, Vol. 30 (Curran Associates, Inc., 2017). URL https://proceedings.neurips.cc/paper_files/paper/2017/file/6449f44a102fde848669bdd9eb6b76fa-Paper.pdf. [16] Goodfellow, I. J., Bengio, Y. & Courville, A. Deep Learning (MIT Press, Cambridge, MA, USA, 2016). http://www.deeplearningbook.org. [17] Vergara, J. R. & Estévez, P. A. A review of feature selection methods based on mutual information. Neural computing and applications 24, 175–186 (2014). [18] Farahani, G. Feature selection based on cross-correlation for the intrusion detection system. Security and Communication Networks 1–17 (2020). Giraud, C. Introduction to High-Dimensional Statistics (Chapman and Hall/CRC, 2015). [11] Cawley, G. C. & Talbot, N. L. C. On over-fitting in model selection and subsequent selection bias in performance evaluation. Journal of Machine Learning Research 11, 2079–2107 (2010). URL http://jmlr.org/papers/v11/cawley10a.html. [12] Dua, D. & Graff, C. Uci machine learning repository (2017). URL http://archive.ics.uci.edu/ml. [13] Sejnowski, T. & Gorman, R. Connectionist Bench (Sonar, Mines vs. Rocks). UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5T01Q. [14] Rafiei, M. Residential Building Data Set. UCI Machine Learning Repository (2018). DOI: https://doi.org/10.24432/C5S896. [15] Ke, G. et al. Guyon, I. et al. (eds) Lightgbm: A highly efficient gradient boosting decision tree. (eds Guyon, I. et al.) Advances in Neural Information Processing Systems, Vol. 30 (Curran Associates, Inc., 2017). URL https://proceedings.neurips.cc/paper_files/paper/2017/file/6449f44a102fde848669bdd9eb6b76fa-Paper.pdf. [16] Goodfellow, I. J., Bengio, Y. & Courville, A. Deep Learning (MIT Press, Cambridge, MA, USA, 2016). http://www.deeplearningbook.org. [17] Vergara, J. R. & Estévez, P. A. A review of feature selection methods based on mutual information. Neural computing and applications 24, 175–186 (2014). [18] Farahani, G. Feature selection based on cross-correlation for the intrusion detection system. Security and Communication Networks 1–17 (2020). Cawley, G. C. & Talbot, N. L. C. On over-fitting in model selection and subsequent selection bias in performance evaluation. Journal of Machine Learning Research 11, 2079–2107 (2010). URL http://jmlr.org/papers/v11/cawley10a.html. [12] Dua, D. & Graff, C. Uci machine learning repository (2017). URL http://archive.ics.uci.edu/ml. [13] Sejnowski, T. & Gorman, R. Connectionist Bench (Sonar, Mines vs. Rocks). UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5T01Q. [14] Rafiei, M. Residential Building Data Set. UCI Machine Learning Repository (2018). DOI: https://doi.org/10.24432/C5S896. [15] Ke, G. et al. Guyon, I. et al. (eds) Lightgbm: A highly efficient gradient boosting decision tree. (eds Guyon, I. et al.) Advances in Neural Information Processing Systems, Vol. 30 (Curran Associates, Inc., 2017). URL https://proceedings.neurips.cc/paper_files/paper/2017/file/6449f44a102fde848669bdd9eb6b76fa-Paper.pdf. 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Deep Learning (MIT Press, Cambridge, MA, USA, 2016). http://www.deeplearningbook.org. [17] Vergara, J. R. & Estévez, P. A. A review of feature selection methods based on mutual information. Neural computing and applications 24, 175–186 (2014). [18] Farahani, G. Feature selection based on cross-correlation for the intrusion detection system. Security and Communication Networks 1–17 (2020). Vinh, L. T., Lee, S., Park, Y.-T. & d’Auriol, B. J. A novel feature selection method based on normalized mutual information. Applied Intelligence 37, 100–120 (2012). [9] Cover, T. & Thomas, J. Elements of Information Theory (Wiley-Interscience, 2006). [10] Giraud, C. Introduction to High-Dimensional Statistics (Chapman and Hall/CRC, 2015). [11] Cawley, G. C. & Talbot, N. L. C. On over-fitting in model selection and subsequent selection bias in performance evaluation. Journal of Machine Learning Research 11, 2079–2107 (2010). URL http://jmlr.org/papers/v11/cawley10a.html. [12] Dua, D. & Graff, C. Uci machine learning repository (2017). URL http://archive.ics.uci.edu/ml. [13] Sejnowski, T. & Gorman, R. Connectionist Bench (Sonar, Mines vs. Rocks). UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5T01Q. [14] Rafiei, M. Residential Building Data Set. UCI Machine Learning Repository (2018). DOI: https://doi.org/10.24432/C5S896. [15] Ke, G. et al. Guyon, I. et al. (eds) Lightgbm: A highly efficient gradient boosting decision tree. (eds Guyon, I. et al.) Advances in Neural Information Processing Systems, Vol. 30 (Curran Associates, Inc., 2017). URL https://proceedings.neurips.cc/paper_files/paper/2017/file/6449f44a102fde848669bdd9eb6b76fa-Paper.pdf. [16] Goodfellow, I. J., Bengio, Y. & Courville, A. Deep Learning (MIT Press, Cambridge, MA, USA, 2016). http://www.deeplearningbook.org. [17] Vergara, J. R. & Estévez, P. A. A review of feature selection methods based on mutual information. Neural computing and applications 24, 175–186 (2014). [18] Farahani, G. 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  4. Wrapper feature selection with partially labeled data. Applied Intelligence 52, 12316–12329 (2022). [5] Zhou, H., Wang, X. & Zhu, R. Feature selection based on mutual information with correlation coefficient. Applied Intelligence 1–18 (2022). [6] Pudjihartono, N., Fadason, T., Kempa-Liehr, A. W. & O’Sullivan, J. M. A review of feature selection methods for machine learning-based disease risk prediction. Frontiers in Bioinformatics 2, 927312 (2022). [7] Guyon, I. & Elisseeff, A. An introduction to variable and feature selection. Journal of machine learning research 3, 1157–1182 (2003). [8] Vinh, L. T., Lee, S., Park, Y.-T. & d’Auriol, B. J. A novel feature selection method based on normalized mutual information. Applied Intelligence 37, 100–120 (2012). [9] Cover, T. & Thomas, J. Elements of Information Theory (Wiley-Interscience, 2006). [10] Giraud, C. Introduction to High-Dimensional Statistics (Chapman and Hall/CRC, 2015). [11] Cawley, G. C. & Talbot, N. L. C. 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Deep Learning (MIT Press, Cambridge, MA, USA, 2016). http://www.deeplearningbook.org. [17] Vergara, J. R. & Estévez, P. A. A review of feature selection methods based on mutual information. Neural computing and applications 24, 175–186 (2014). [18] Farahani, G. Feature selection based on cross-correlation for the intrusion detection system. Security and Communication Networks 1–17 (2020). Zhou, H., Wang, X. & Zhu, R. Feature selection based on mutual information with correlation coefficient. Applied Intelligence 1–18 (2022). [6] Pudjihartono, N., Fadason, T., Kempa-Liehr, A. W. & O’Sullivan, J. M. A review of feature selection methods for machine learning-based disease risk prediction. Frontiers in Bioinformatics 2, 927312 (2022). [7] Guyon, I. & Elisseeff, A. An introduction to variable and feature selection. Journal of machine learning research 3, 1157–1182 (2003). [8] Vinh, L. T., Lee, S., Park, Y.-T. & d’Auriol, B. J. 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(eds) Lightgbm: A highly efficient gradient boosting decision tree. (eds Guyon, I. et al.) Advances in Neural Information Processing Systems, Vol. 30 (Curran Associates, Inc., 2017). URL https://proceedings.neurips.cc/paper_files/paper/2017/file/6449f44a102fde848669bdd9eb6b76fa-Paper.pdf. [16] Goodfellow, I. J., Bengio, Y. & Courville, A. Deep Learning (MIT Press, Cambridge, MA, USA, 2016). http://www.deeplearningbook.org. [17] Vergara, J. R. & Estévez, P. A. A review of feature selection methods based on mutual information. Neural computing and applications 24, 175–186 (2014). [18] Farahani, G. Feature selection based on cross-correlation for the intrusion detection system. Security and Communication Networks 1–17 (2020). Pudjihartono, N., Fadason, T., Kempa-Liehr, A. W. & O’Sullivan, J. M. A review of feature selection methods for machine learning-based disease risk prediction. Frontiers in Bioinformatics 2, 927312 (2022). [7] Guyon, I. & Elisseeff, A. An introduction to variable and feature selection. Journal of machine learning research 3, 1157–1182 (2003). [8] Vinh, L. T., Lee, S., Park, Y.-T. & d’Auriol, B. J. A novel feature selection method based on normalized mutual information. Applied Intelligence 37, 100–120 (2012). [9] Cover, T. & Thomas, J. Elements of Information Theory (Wiley-Interscience, 2006). [10] Giraud, C. Introduction to High-Dimensional Statistics (Chapman and Hall/CRC, 2015). [11] Cawley, G. C. & Talbot, N. L. C. On over-fitting in model selection and subsequent selection bias in performance evaluation. Journal of Machine Learning Research 11, 2079–2107 (2010). URL http://jmlr.org/papers/v11/cawley10a.html. [12] Dua, D. & Graff, C. Uci machine learning repository (2017). URL http://archive.ics.uci.edu/ml. [13] Sejnowski, T. & Gorman, R. Connectionist Bench (Sonar, Mines vs. Rocks). UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5T01Q. [14] Rafiei, M. Residential Building Data Set. UCI Machine Learning Repository (2018). DOI: https://doi.org/10.24432/C5S896. [15] Ke, G. et al. Guyon, I. et al. (eds) Lightgbm: A highly efficient gradient boosting decision tree. (eds Guyon, I. et al.) Advances in Neural Information Processing Systems, Vol. 30 (Curran Associates, Inc., 2017). URL https://proceedings.neurips.cc/paper_files/paper/2017/file/6449f44a102fde848669bdd9eb6b76fa-Paper.pdf. [16] Goodfellow, I. J., Bengio, Y. & Courville, A. Deep Learning (MIT Press, Cambridge, MA, USA, 2016). http://www.deeplearningbook.org. [17] Vergara, J. R. & Estévez, P. A. A review of feature selection methods based on mutual information. Neural computing and applications 24, 175–186 (2014). [18] Farahani, G. Feature selection based on cross-correlation for the intrusion detection system. Security and Communication Networks 1–17 (2020). Guyon, I. & Elisseeff, A. An introduction to variable and feature selection. Journal of machine learning research 3, 1157–1182 (2003). [8] Vinh, L. T., Lee, S., Park, Y.-T. & d’Auriol, B. J. A novel feature selection method based on normalized mutual information. Applied Intelligence 37, 100–120 (2012). [9] Cover, T. & Thomas, J. Elements of Information Theory (Wiley-Interscience, 2006). [10] Giraud, C. Introduction to High-Dimensional Statistics (Chapman and Hall/CRC, 2015). [11] Cawley, G. C. & Talbot, N. L. C. On over-fitting in model selection and subsequent selection bias in performance evaluation. Journal of Machine Learning Research 11, 2079–2107 (2010). URL http://jmlr.org/papers/v11/cawley10a.html. [12] Dua, D. & Graff, C. Uci machine learning repository (2017). URL http://archive.ics.uci.edu/ml. [13] Sejnowski, T. & Gorman, R. Connectionist Bench (Sonar, Mines vs. Rocks). UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5T01Q. [14] Rafiei, M. Residential Building Data Set. UCI Machine Learning Repository (2018). DOI: https://doi.org/10.24432/C5S896. [15] Ke, G. et al. Guyon, I. et al. (eds) Lightgbm: A highly efficient gradient boosting decision tree. (eds Guyon, I. et al.) Advances in Neural Information Processing Systems, Vol. 30 (Curran Associates, Inc., 2017). URL https://proceedings.neurips.cc/paper_files/paper/2017/file/6449f44a102fde848669bdd9eb6b76fa-Paper.pdf. [16] Goodfellow, I. J., Bengio, Y. & Courville, A. Deep Learning (MIT Press, Cambridge, MA, USA, 2016). http://www.deeplearningbook.org. [17] Vergara, J. R. & Estévez, P. A. A review of feature selection methods based on mutual information. Neural computing and applications 24, 175–186 (2014). [18] Farahani, G. Feature selection based on cross-correlation for the intrusion detection system. Security and Communication Networks 1–17 (2020). Vinh, L. T., Lee, S., Park, Y.-T. & d’Auriol, B. J. A novel feature selection method based on normalized mutual information. Applied Intelligence 37, 100–120 (2012). [9] Cover, T. & Thomas, J. Elements of Information Theory (Wiley-Interscience, 2006). [10] Giraud, C. Introduction to High-Dimensional Statistics (Chapman and Hall/CRC, 2015). [11] Cawley, G. C. & Talbot, N. L. C. On over-fitting in model selection and subsequent selection bias in performance evaluation. Journal of Machine Learning Research 11, 2079–2107 (2010). URL http://jmlr.org/papers/v11/cawley10a.html. [12] Dua, D. & Graff, C. Uci machine learning repository (2017). URL http://archive.ics.uci.edu/ml. [13] Sejnowski, T. & Gorman, R. Connectionist Bench (Sonar, Mines vs. Rocks). UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5T01Q. [14] Rafiei, M. Residential Building Data Set. UCI Machine Learning Repository (2018). DOI: https://doi.org/10.24432/C5S896. [15] Ke, G. et al. Guyon, I. et al. (eds) Lightgbm: A highly efficient gradient boosting decision tree. (eds Guyon, I. et al.) Advances in Neural Information Processing Systems, Vol. 30 (Curran Associates, Inc., 2017). URL https://proceedings.neurips.cc/paper_files/paper/2017/file/6449f44a102fde848669bdd9eb6b76fa-Paper.pdf. [16] Goodfellow, I. J., Bengio, Y. & Courville, A. Deep Learning (MIT Press, Cambridge, MA, USA, 2016). http://www.deeplearningbook.org. [17] Vergara, J. R. & Estévez, P. A. A review of feature selection methods based on mutual information. Neural computing and applications 24, 175–186 (2014). [18] Farahani, G. Feature selection based on cross-correlation for the intrusion detection system. Security and Communication Networks 1–17 (2020). Cover, T. & Thomas, J. Elements of Information Theory (Wiley-Interscience, 2006). [10] Giraud, C. Introduction to High-Dimensional Statistics (Chapman and Hall/CRC, 2015). [11] Cawley, G. C. & Talbot, N. L. C. On over-fitting in model selection and subsequent selection bias in performance evaluation. Journal of Machine Learning Research 11, 2079–2107 (2010). URL http://jmlr.org/papers/v11/cawley10a.html. [12] Dua, D. & Graff, C. Uci machine learning repository (2017). URL http://archive.ics.uci.edu/ml. [13] Sejnowski, T. & Gorman, R. Connectionist Bench (Sonar, Mines vs. Rocks). UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5T01Q. [14] Rafiei, M. Residential Building Data Set. UCI Machine Learning Repository (2018). DOI: https://doi.org/10.24432/C5S896. [15] Ke, G. et al. Guyon, I. et al. (eds) Lightgbm: A highly efficient gradient boosting decision tree. (eds Guyon, I. et al.) Advances in Neural Information Processing Systems, Vol. 30 (Curran Associates, Inc., 2017). URL https://proceedings.neurips.cc/paper_files/paper/2017/file/6449f44a102fde848669bdd9eb6b76fa-Paper.pdf. [16] Goodfellow, I. J., Bengio, Y. & Courville, A. Deep Learning (MIT Press, Cambridge, MA, USA, 2016). http://www.deeplearningbook.org. [17] Vergara, J. R. & Estévez, P. A. A review of feature selection methods based on mutual information. Neural computing and applications 24, 175–186 (2014). [18] Farahani, G. Feature selection based on cross-correlation for the intrusion detection system. Security and Communication Networks 1–17 (2020). Giraud, C. Introduction to High-Dimensional Statistics (Chapman and Hall/CRC, 2015). [11] Cawley, G. C. & Talbot, N. L. C. On over-fitting in model selection and subsequent selection bias in performance evaluation. Journal of Machine Learning Research 11, 2079–2107 (2010). URL http://jmlr.org/papers/v11/cawley10a.html. [12] Dua, D. & Graff, C. Uci machine learning repository (2017). URL http://archive.ics.uci.edu/ml. [13] Sejnowski, T. & Gorman, R. Connectionist Bench (Sonar, Mines vs. Rocks). UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5T01Q. [14] Rafiei, M. Residential Building Data Set. UCI Machine Learning Repository (2018). DOI: https://doi.org/10.24432/C5S896. [15] Ke, G. et al. Guyon, I. et al. (eds) Lightgbm: A highly efficient gradient boosting decision tree. (eds Guyon, I. et al.) Advances in Neural Information Processing Systems, Vol. 30 (Curran Associates, Inc., 2017). URL https://proceedings.neurips.cc/paper_files/paper/2017/file/6449f44a102fde848669bdd9eb6b76fa-Paper.pdf. [16] Goodfellow, I. J., Bengio, Y. & Courville, A. Deep Learning (MIT Press, Cambridge, MA, USA, 2016). http://www.deeplearningbook.org. [17] Vergara, J. R. & Estévez, P. A. A review of feature selection methods based on mutual information. Neural computing and applications 24, 175–186 (2014). [18] Farahani, G. Feature selection based on cross-correlation for the intrusion detection system. Security and Communication Networks 1–17 (2020). Cawley, G. C. & Talbot, N. L. C. On over-fitting in model selection and subsequent selection bias in performance evaluation. Journal of Machine Learning Research 11, 2079–2107 (2010). URL http://jmlr.org/papers/v11/cawley10a.html. [12] Dua, D. & Graff, C. Uci machine learning repository (2017). URL http://archive.ics.uci.edu/ml. [13] Sejnowski, T. & Gorman, R. Connectionist Bench (Sonar, Mines vs. Rocks). UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5T01Q. [14] Rafiei, M. Residential Building Data Set. UCI Machine Learning Repository (2018). DOI: https://doi.org/10.24432/C5S896. [15] Ke, G. et al. Guyon, I. et al. (eds) Lightgbm: A highly efficient gradient boosting decision tree. (eds Guyon, I. et al.) Advances in Neural Information Processing Systems, Vol. 30 (Curran Associates, Inc., 2017). URL https://proceedings.neurips.cc/paper_files/paper/2017/file/6449f44a102fde848669bdd9eb6b76fa-Paper.pdf. [16] Goodfellow, I. J., Bengio, Y. & Courville, A. Deep Learning (MIT Press, Cambridge, MA, USA, 2016). http://www.deeplearningbook.org. [17] Vergara, J. R. & Estévez, P. A. A review of feature selection methods based on mutual information. Neural computing and applications 24, 175–186 (2014). [18] Farahani, G. Feature selection based on cross-correlation for the intrusion detection system. 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Neural computing and applications 24, 175–186 (2014). [18] Farahani, G. Feature selection based on cross-correlation for the intrusion detection system. Security and Communication Networks 1–17 (2020). Sejnowski, T. & Gorman, R. Connectionist Bench (Sonar, Mines vs. Rocks). UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5T01Q. [14] Rafiei, M. Residential Building Data Set. UCI Machine Learning Repository (2018). DOI: https://doi.org/10.24432/C5S896. [15] Ke, G. et al. Guyon, I. et al. (eds) Lightgbm: A highly efficient gradient boosting decision tree. (eds Guyon, I. et al.) Advances in Neural Information Processing Systems, Vol. 30 (Curran Associates, Inc., 2017). URL https://proceedings.neurips.cc/paper_files/paper/2017/file/6449f44a102fde848669bdd9eb6b76fa-Paper.pdf. [16] Goodfellow, I. J., Bengio, Y. & Courville, A. Deep Learning (MIT Press, Cambridge, MA, USA, 2016). http://www.deeplearningbook.org. [17] Vergara, J. R. & Estévez, P. A. 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Neural computing and applications 24, 175–186 (2014). [18] Farahani, G. Feature selection based on cross-correlation for the intrusion detection system. Security and Communication Networks 1–17 (2020). Ke, G. et al. Guyon, I. et al. (eds) Lightgbm: A highly efficient gradient boosting decision tree. (eds Guyon, I. et al.) Advances in Neural Information Processing Systems, Vol. 30 (Curran Associates, Inc., 2017). URL https://proceedings.neurips.cc/paper_files/paper/2017/file/6449f44a102fde848669bdd9eb6b76fa-Paper.pdf. [16] Goodfellow, I. J., Bengio, Y. & Courville, A. Deep Learning (MIT Press, Cambridge, MA, USA, 2016). http://www.deeplearningbook.org. [17] Vergara, J. R. & Estévez, P. A. A review of feature selection methods based on mutual information. Neural computing and applications 24, 175–186 (2014). [18] Farahani, G. Feature selection based on cross-correlation for the intrusion detection system. Security and Communication Networks 1–17 (2020). 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URL http://jmlr.org/papers/v11/cawley10a.html. [12] Dua, D. & Graff, C. Uci machine learning repository (2017). URL http://archive.ics.uci.edu/ml. [13] Sejnowski, T. & Gorman, R. Connectionist Bench (Sonar, Mines vs. Rocks). UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5T01Q. [14] Rafiei, M. Residential Building Data Set. UCI Machine Learning Repository (2018). DOI: https://doi.org/10.24432/C5S896. [15] Ke, G. et al. Guyon, I. et al. (eds) Lightgbm: A highly efficient gradient boosting decision tree. (eds Guyon, I. et al.) Advances in Neural Information Processing Systems, Vol. 30 (Curran Associates, Inc., 2017). URL https://proceedings.neurips.cc/paper_files/paper/2017/file/6449f44a102fde848669bdd9eb6b76fa-Paper.pdf. [16] Goodfellow, I. J., Bengio, Y. & Courville, A. Deep Learning (MIT Press, Cambridge, MA, USA, 2016). http://www.deeplearningbook.org. [17] Vergara, J. R. & Estévez, P. A. A review of feature selection methods based on mutual information. Neural computing and applications 24, 175–186 (2014). [18] Farahani, G. Feature selection based on cross-correlation for the intrusion detection system. Security and Communication Networks 1–17 (2020). Pudjihartono, N., Fadason, T., Kempa-Liehr, A. W. & O’Sullivan, J. M. A review of feature selection methods for machine learning-based disease risk prediction. Frontiers in Bioinformatics 2, 927312 (2022). [7] Guyon, I. & Elisseeff, A. An introduction to variable and feature selection. Journal of machine learning research 3, 1157–1182 (2003). [8] Vinh, L. T., Lee, S., Park, Y.-T. & d’Auriol, B. J. A novel feature selection method based on normalized mutual information. Applied Intelligence 37, 100–120 (2012). [9] Cover, T. & Thomas, J. Elements of Information Theory (Wiley-Interscience, 2006). [10] Giraud, C. Introduction to High-Dimensional Statistics (Chapman and Hall/CRC, 2015). [11] Cawley, G. C. & Talbot, N. L. C. On over-fitting in model selection and subsequent selection bias in performance evaluation. Journal of Machine Learning Research 11, 2079–2107 (2010). URL http://jmlr.org/papers/v11/cawley10a.html. [12] Dua, D. & Graff, C. Uci machine learning repository (2017). URL http://archive.ics.uci.edu/ml. [13] Sejnowski, T. & Gorman, R. Connectionist Bench (Sonar, Mines vs. Rocks). UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5T01Q. [14] Rafiei, M. Residential Building Data Set. UCI Machine Learning Repository (2018). DOI: https://doi.org/10.24432/C5S896. [15] Ke, G. et al. Guyon, I. et al. (eds) Lightgbm: A highly efficient gradient boosting decision tree. (eds Guyon, I. et al.) Advances in Neural Information Processing Systems, Vol. 30 (Curran Associates, Inc., 2017). URL https://proceedings.neurips.cc/paper_files/paper/2017/file/6449f44a102fde848669bdd9eb6b76fa-Paper.pdf. [16] Goodfellow, I. J., Bengio, Y. & Courville, A. Deep Learning (MIT Press, Cambridge, MA, USA, 2016). http://www.deeplearningbook.org. [17] Vergara, J. R. & Estévez, P. A. A review of feature selection methods based on mutual information. Neural computing and applications 24, 175–186 (2014). [18] Farahani, G. Feature selection based on cross-correlation for the intrusion detection system. Security and Communication Networks 1–17 (2020). Guyon, I. & Elisseeff, A. An introduction to variable and feature selection. Journal of machine learning research 3, 1157–1182 (2003). [8] Vinh, L. T., Lee, S., Park, Y.-T. & d’Auriol, B. J. A novel feature selection method based on normalized mutual information. Applied Intelligence 37, 100–120 (2012). [9] Cover, T. & Thomas, J. Elements of Information Theory (Wiley-Interscience, 2006). [10] Giraud, C. Introduction to High-Dimensional Statistics (Chapman and Hall/CRC, 2015). [11] Cawley, G. C. & Talbot, N. L. C. On over-fitting in model selection and subsequent selection bias in performance evaluation. Journal of Machine Learning Research 11, 2079–2107 (2010). URL http://jmlr.org/papers/v11/cawley10a.html. [12] Dua, D. & Graff, C. Uci machine learning repository (2017). URL http://archive.ics.uci.edu/ml. [13] Sejnowski, T. & Gorman, R. Connectionist Bench (Sonar, Mines vs. Rocks). UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5T01Q. [14] Rafiei, M. Residential Building Data Set. UCI Machine Learning Repository (2018). DOI: https://doi.org/10.24432/C5S896. [15] Ke, G. et al. Guyon, I. et al. (eds) Lightgbm: A highly efficient gradient boosting decision tree. (eds Guyon, I. et al.) Advances in Neural Information Processing Systems, Vol. 30 (Curran Associates, Inc., 2017). URL https://proceedings.neurips.cc/paper_files/paper/2017/file/6449f44a102fde848669bdd9eb6b76fa-Paper.pdf. [16] Goodfellow, I. J., Bengio, Y. & Courville, A. Deep Learning (MIT Press, Cambridge, MA, USA, 2016). http://www.deeplearningbook.org. [17] Vergara, J. R. & Estévez, P. A. A review of feature selection methods based on mutual information. Neural computing and applications 24, 175–186 (2014). [18] Farahani, G. Feature selection based on cross-correlation for the intrusion detection system. Security and Communication Networks 1–17 (2020). Vinh, L. T., Lee, S., Park, Y.-T. & d’Auriol, B. J. A novel feature selection method based on normalized mutual information. Applied Intelligence 37, 100–120 (2012). [9] Cover, T. & Thomas, J. Elements of Information Theory (Wiley-Interscience, 2006). [10] Giraud, C. Introduction to High-Dimensional Statistics (Chapman and Hall/CRC, 2015). [11] Cawley, G. C. & Talbot, N. L. C. On over-fitting in model selection and subsequent selection bias in performance evaluation. Journal of Machine Learning Research 11, 2079–2107 (2010). URL http://jmlr.org/papers/v11/cawley10a.html. [12] Dua, D. & Graff, C. Uci machine learning repository (2017). URL http://archive.ics.uci.edu/ml. [13] Sejnowski, T. & Gorman, R. Connectionist Bench (Sonar, Mines vs. Rocks). UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5T01Q. [14] Rafiei, M. Residential Building Data Set. UCI Machine Learning Repository (2018). DOI: https://doi.org/10.24432/C5S896. [15] Ke, G. et al. Guyon, I. et al. (eds) Lightgbm: A highly efficient gradient boosting decision tree. (eds Guyon, I. et al.) Advances in Neural Information Processing Systems, Vol. 30 (Curran Associates, Inc., 2017). URL https://proceedings.neurips.cc/paper_files/paper/2017/file/6449f44a102fde848669bdd9eb6b76fa-Paper.pdf. [16] Goodfellow, I. J., Bengio, Y. & Courville, A. Deep Learning (MIT Press, Cambridge, MA, USA, 2016). http://www.deeplearningbook.org. [17] Vergara, J. R. & Estévez, P. A. A review of feature selection methods based on mutual information. Neural computing and applications 24, 175–186 (2014). [18] Farahani, G. Feature selection based on cross-correlation for the intrusion detection system. Security and Communication Networks 1–17 (2020). Cover, T. & Thomas, J. Elements of Information Theory (Wiley-Interscience, 2006). [10] Giraud, C. Introduction to High-Dimensional Statistics (Chapman and Hall/CRC, 2015). [11] Cawley, G. C. & Talbot, N. L. C. On over-fitting in model selection and subsequent selection bias in performance evaluation. Journal of Machine Learning Research 11, 2079–2107 (2010). URL http://jmlr.org/papers/v11/cawley10a.html. [12] Dua, D. & Graff, C. Uci machine learning repository (2017). URL http://archive.ics.uci.edu/ml. [13] Sejnowski, T. & Gorman, R. Connectionist Bench (Sonar, Mines vs. Rocks). UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5T01Q. [14] Rafiei, M. Residential Building Data Set. UCI Machine Learning Repository (2018). DOI: https://doi.org/10.24432/C5S896. [15] Ke, G. et al. Guyon, I. et al. (eds) Lightgbm: A highly efficient gradient boosting decision tree. (eds Guyon, I. et al.) Advances in Neural Information Processing Systems, Vol. 30 (Curran Associates, Inc., 2017). URL https://proceedings.neurips.cc/paper_files/paper/2017/file/6449f44a102fde848669bdd9eb6b76fa-Paper.pdf. [16] Goodfellow, I. J., Bengio, Y. & Courville, A. Deep Learning (MIT Press, Cambridge, MA, USA, 2016). http://www.deeplearningbook.org. [17] Vergara, J. R. & Estévez, P. A. A review of feature selection methods based on mutual information. Neural computing and applications 24, 175–186 (2014). [18] Farahani, G. Feature selection based on cross-correlation for the intrusion detection system. Security and Communication Networks 1–17 (2020). Giraud, C. Introduction to High-Dimensional Statistics (Chapman and Hall/CRC, 2015). [11] Cawley, G. C. & Talbot, N. L. C. On over-fitting in model selection and subsequent selection bias in performance evaluation. Journal of Machine Learning Research 11, 2079–2107 (2010). URL http://jmlr.org/papers/v11/cawley10a.html. [12] Dua, D. & Graff, C. Uci machine learning repository (2017). URL http://archive.ics.uci.edu/ml. [13] Sejnowski, T. & Gorman, R. Connectionist Bench (Sonar, Mines vs. Rocks). UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5T01Q. [14] Rafiei, M. Residential Building Data Set. UCI Machine Learning Repository (2018). DOI: https://doi.org/10.24432/C5S896. [15] Ke, G. et al. Guyon, I. et al. (eds) Lightgbm: A highly efficient gradient boosting decision tree. (eds Guyon, I. et al.) Advances in Neural Information Processing Systems, Vol. 30 (Curran Associates, Inc., 2017). URL https://proceedings.neurips.cc/paper_files/paper/2017/file/6449f44a102fde848669bdd9eb6b76fa-Paper.pdf. [16] Goodfellow, I. J., Bengio, Y. & Courville, A. Deep Learning (MIT Press, Cambridge, MA, USA, 2016). http://www.deeplearningbook.org. [17] Vergara, J. R. & Estévez, P. A. A review of feature selection methods based on mutual information. Neural computing and applications 24, 175–186 (2014). [18] Farahani, G. Feature selection based on cross-correlation for the intrusion detection system. Security and Communication Networks 1–17 (2020). Cawley, G. C. & Talbot, N. L. C. On over-fitting in model selection and subsequent selection bias in performance evaluation. Journal of Machine Learning Research 11, 2079–2107 (2010). URL http://jmlr.org/papers/v11/cawley10a.html. [12] Dua, D. & Graff, C. Uci machine learning repository (2017). URL http://archive.ics.uci.edu/ml. [13] Sejnowski, T. & Gorman, R. Connectionist Bench (Sonar, Mines vs. Rocks). UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5T01Q. [14] Rafiei, M. Residential Building Data Set. UCI Machine Learning Repository (2018). DOI: https://doi.org/10.24432/C5S896. [15] Ke, G. et al. Guyon, I. et al. (eds) Lightgbm: A highly efficient gradient boosting decision tree. (eds Guyon, I. et al.) Advances in Neural Information Processing Systems, Vol. 30 (Curran Associates, Inc., 2017). URL https://proceedings.neurips.cc/paper_files/paper/2017/file/6449f44a102fde848669bdd9eb6b76fa-Paper.pdf. [16] Goodfellow, I. J., Bengio, Y. & Courville, A. Deep Learning (MIT Press, Cambridge, MA, USA, 2016). http://www.deeplearningbook.org. [17] Vergara, J. R. & Estévez, P. A. A review of feature selection methods based on mutual information. Neural computing and applications 24, 175–186 (2014). [18] Farahani, G. Feature selection based on cross-correlation for the intrusion detection system. Security and Communication Networks 1–17 (2020). Dua, D. & Graff, C. Uci machine learning repository (2017). URL http://archive.ics.uci.edu/ml. [13] Sejnowski, T. & Gorman, R. Connectionist Bench (Sonar, Mines vs. Rocks). UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5T01Q. [14] Rafiei, M. Residential Building Data Set. UCI Machine Learning Repository (2018). DOI: https://doi.org/10.24432/C5S896. [15] Ke, G. et al. Guyon, I. et al. (eds) Lightgbm: A highly efficient gradient boosting decision tree. (eds Guyon, I. et al.) Advances in Neural Information Processing Systems, Vol. 30 (Curran Associates, Inc., 2017). URL https://proceedings.neurips.cc/paper_files/paper/2017/file/6449f44a102fde848669bdd9eb6b76fa-Paper.pdf. [16] Goodfellow, I. J., Bengio, Y. & Courville, A. Deep Learning (MIT Press, Cambridge, MA, USA, 2016). http://www.deeplearningbook.org. [17] Vergara, J. R. & Estévez, P. A. A review of feature selection methods based on mutual information. Neural computing and applications 24, 175–186 (2014). [18] Farahani, G. Feature selection based on cross-correlation for the intrusion detection system. Security and Communication Networks 1–17 (2020). Sejnowski, T. & Gorman, R. Connectionist Bench (Sonar, Mines vs. Rocks). UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5T01Q. [14] Rafiei, M. Residential Building Data Set. UCI Machine Learning Repository (2018). DOI: https://doi.org/10.24432/C5S896. [15] Ke, G. et al. Guyon, I. et al. (eds) Lightgbm: A highly efficient gradient boosting decision tree. (eds Guyon, I. et al.) Advances in Neural Information Processing Systems, Vol. 30 (Curran Associates, Inc., 2017). URL https://proceedings.neurips.cc/paper_files/paper/2017/file/6449f44a102fde848669bdd9eb6b76fa-Paper.pdf. [16] Goodfellow, I. J., Bengio, Y. & Courville, A. Deep Learning (MIT Press, Cambridge, MA, USA, 2016). http://www.deeplearningbook.org. [17] Vergara, J. R. & Estévez, P. A. A review of feature selection methods based on mutual information. Neural computing and applications 24, 175–186 (2014). [18] Farahani, G. Feature selection based on cross-correlation for the intrusion detection system. Security and Communication Networks 1–17 (2020). Rafiei, M. Residential Building Data Set. UCI Machine Learning Repository (2018). DOI: https://doi.org/10.24432/C5S896. [15] Ke, G. et al. Guyon, I. et al. (eds) Lightgbm: A highly efficient gradient boosting decision tree. (eds Guyon, I. et al.) Advances in Neural Information Processing Systems, Vol. 30 (Curran Associates, Inc., 2017). URL https://proceedings.neurips.cc/paper_files/paper/2017/file/6449f44a102fde848669bdd9eb6b76fa-Paper.pdf. [16] Goodfellow, I. J., Bengio, Y. & Courville, A. Deep Learning (MIT Press, Cambridge, MA, USA, 2016). http://www.deeplearningbook.org. [17] Vergara, J. R. & Estévez, P. A. A review of feature selection methods based on mutual information. Neural computing and applications 24, 175–186 (2014). [18] Farahani, G. Feature selection based on cross-correlation for the intrusion detection system. Security and Communication Networks 1–17 (2020). Ke, G. et al. Guyon, I. et al. (eds) Lightgbm: A highly efficient gradient boosting decision tree. (eds Guyon, I. et al.) Advances in Neural Information Processing Systems, Vol. 30 (Curran Associates, Inc., 2017). URL https://proceedings.neurips.cc/paper_files/paper/2017/file/6449f44a102fde848669bdd9eb6b76fa-Paper.pdf. [16] Goodfellow, I. J., Bengio, Y. & Courville, A. Deep Learning (MIT Press, Cambridge, MA, USA, 2016). http://www.deeplearningbook.org. [17] Vergara, J. R. & Estévez, P. A. A review of feature selection methods based on mutual information. Neural computing and applications 24, 175–186 (2014). [18] Farahani, G. Feature selection based on cross-correlation for the intrusion detection system. Security and Communication Networks 1–17 (2020). Goodfellow, I. J., Bengio, Y. & Courville, A. Deep Learning (MIT Press, Cambridge, MA, USA, 2016). http://www.deeplearningbook.org. [17] Vergara, J. R. & Estévez, P. A. A review of feature selection methods based on mutual information. Neural computing and applications 24, 175–186 (2014). [18] Farahani, G. Feature selection based on cross-correlation for the intrusion detection system. Security and Communication Networks 1–17 (2020). Vergara, J. R. & Estévez, P. A. A review of feature selection methods based on mutual information. Neural computing and applications 24, 175–186 (2014). [18] Farahani, G. Feature selection based on cross-correlation for the intrusion detection system. Security and Communication Networks 1–17 (2020). Farahani, G. Feature selection based on cross-correlation for the intrusion detection system. Security and Communication Networks 1–17 (2020).
  6. A review of feature selection methods for machine learning-based disease risk prediction. Frontiers in Bioinformatics 2, 927312 (2022). [7] Guyon, I. & Elisseeff, A. An introduction to variable and feature selection. Journal of machine learning research 3, 1157–1182 (2003). [8] Vinh, L. T., Lee, S., Park, Y.-T. & d’Auriol, B. J. A novel feature selection method based on normalized mutual information. Applied Intelligence 37, 100–120 (2012). [9] Cover, T. & Thomas, J. Elements of Information Theory (Wiley-Interscience, 2006). [10] Giraud, C. Introduction to High-Dimensional Statistics (Chapman and Hall/CRC, 2015). [11] Cawley, G. C. & Talbot, N. L. C. On over-fitting in model selection and subsequent selection bias in performance evaluation. Journal of Machine Learning Research 11, 2079–2107 (2010). URL http://jmlr.org/papers/v11/cawley10a.html. [12] Dua, D. & Graff, C. Uci machine learning repository (2017). URL http://archive.ics.uci.edu/ml. [13] Sejnowski, T. & Gorman, R. Connectionist Bench (Sonar, Mines vs. Rocks). UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5T01Q. [14] Rafiei, M. Residential Building Data Set. UCI Machine Learning Repository (2018). DOI: https://doi.org/10.24432/C5S896. [15] Ke, G. et al. Guyon, I. et al. (eds) Lightgbm: A highly efficient gradient boosting decision tree. (eds Guyon, I. et al.) Advances in Neural Information Processing Systems, Vol. 30 (Curran Associates, Inc., 2017). URL https://proceedings.neurips.cc/paper_files/paper/2017/file/6449f44a102fde848669bdd9eb6b76fa-Paper.pdf. [16] Goodfellow, I. J., Bengio, Y. & Courville, A. Deep Learning (MIT Press, Cambridge, MA, USA, 2016). http://www.deeplearningbook.org. [17] Vergara, J. R. & Estévez, P. A. A review of feature selection methods based on mutual information. Neural computing and applications 24, 175–186 (2014). [18] Farahani, G. Feature selection based on cross-correlation for the intrusion detection system. Security and Communication Networks 1–17 (2020). Guyon, I. & Elisseeff, A. An introduction to variable and feature selection. Journal of machine learning research 3, 1157–1182 (2003). [8] Vinh, L. T., Lee, S., Park, Y.-T. & d’Auriol, B. J. A novel feature selection method based on normalized mutual information. Applied Intelligence 37, 100–120 (2012). [9] Cover, T. & Thomas, J. Elements of Information Theory (Wiley-Interscience, 2006). [10] Giraud, C. Introduction to High-Dimensional Statistics (Chapman and Hall/CRC, 2015). [11] Cawley, G. C. & Talbot, N. L. C. On over-fitting in model selection and subsequent selection bias in performance evaluation. Journal of Machine Learning Research 11, 2079–2107 (2010). URL http://jmlr.org/papers/v11/cawley10a.html. [12] Dua, D. & Graff, C. Uci machine learning repository (2017). URL http://archive.ics.uci.edu/ml. [13] Sejnowski, T. & Gorman, R. Connectionist Bench (Sonar, Mines vs. Rocks). UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5T01Q. [14] Rafiei, M. Residential Building Data Set. UCI Machine Learning Repository (2018). DOI: https://doi.org/10.24432/C5S896. [15] Ke, G. et al. Guyon, I. et al. (eds) Lightgbm: A highly efficient gradient boosting decision tree. (eds Guyon, I. et al.) Advances in Neural Information Processing Systems, Vol. 30 (Curran Associates, Inc., 2017). URL https://proceedings.neurips.cc/paper_files/paper/2017/file/6449f44a102fde848669bdd9eb6b76fa-Paper.pdf. [16] Goodfellow, I. J., Bengio, Y. & Courville, A. Deep Learning (MIT Press, Cambridge, MA, USA, 2016). http://www.deeplearningbook.org. [17] Vergara, J. R. & Estévez, P. A. A review of feature selection methods based on mutual information. Neural computing and applications 24, 175–186 (2014). [18] Farahani, G. Feature selection based on cross-correlation for the intrusion detection system. Security and Communication Networks 1–17 (2020). Vinh, L. T., Lee, S., Park, Y.-T. & d’Auriol, B. J. A novel feature selection method based on normalized mutual information. Applied Intelligence 37, 100–120 (2012). [9] Cover, T. & Thomas, J. Elements of Information Theory (Wiley-Interscience, 2006). [10] Giraud, C. Introduction to High-Dimensional Statistics (Chapman and Hall/CRC, 2015). [11] Cawley, G. C. & Talbot, N. L. C. On over-fitting in model selection and subsequent selection bias in performance evaluation. Journal of Machine Learning Research 11, 2079–2107 (2010). URL http://jmlr.org/papers/v11/cawley10a.html. [12] Dua, D. & Graff, C. Uci machine learning repository (2017). URL http://archive.ics.uci.edu/ml. [13] Sejnowski, T. & Gorman, R. Connectionist Bench (Sonar, Mines vs. Rocks). UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5T01Q. [14] Rafiei, M. Residential Building Data Set. UCI Machine Learning Repository (2018). DOI: https://doi.org/10.24432/C5S896. [15] Ke, G. et al. Guyon, I. et al. (eds) Lightgbm: A highly efficient gradient boosting decision tree. (eds Guyon, I. et al.) Advances in Neural Information Processing Systems, Vol. 30 (Curran Associates, Inc., 2017). URL https://proceedings.neurips.cc/paper_files/paper/2017/file/6449f44a102fde848669bdd9eb6b76fa-Paper.pdf. [16] Goodfellow, I. J., Bengio, Y. & Courville, A. Deep Learning (MIT Press, Cambridge, MA, USA, 2016). http://www.deeplearningbook.org. [17] Vergara, J. R. & Estévez, P. A. A review of feature selection methods based on mutual information. Neural computing and applications 24, 175–186 (2014). [18] Farahani, G. Feature selection based on cross-correlation for the intrusion detection system. Security and Communication Networks 1–17 (2020). Cover, T. & Thomas, J. Elements of Information Theory (Wiley-Interscience, 2006). [10] Giraud, C. Introduction to High-Dimensional Statistics (Chapman and Hall/CRC, 2015). [11] Cawley, G. C. & Talbot, N. L. C. On over-fitting in model selection and subsequent selection bias in performance evaluation. Journal of Machine Learning Research 11, 2079–2107 (2010). URL http://jmlr.org/papers/v11/cawley10a.html. [12] Dua, D. & Graff, C. Uci machine learning repository (2017). URL http://archive.ics.uci.edu/ml. [13] Sejnowski, T. & Gorman, R. Connectionist Bench (Sonar, Mines vs. Rocks). UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5T01Q. [14] Rafiei, M. Residential Building Data Set. UCI Machine Learning Repository (2018). DOI: https://doi.org/10.24432/C5S896. [15] Ke, G. et al. Guyon, I. et al. (eds) Lightgbm: A highly efficient gradient boosting decision tree. (eds Guyon, I. et al.) Advances in Neural Information Processing Systems, Vol. 30 (Curran Associates, Inc., 2017). URL https://proceedings.neurips.cc/paper_files/paper/2017/file/6449f44a102fde848669bdd9eb6b76fa-Paper.pdf. [16] Goodfellow, I. J., Bengio, Y. & Courville, A. Deep Learning (MIT Press, Cambridge, MA, USA, 2016). http://www.deeplearningbook.org. [17] Vergara, J. R. & Estévez, P. A. A review of feature selection methods based on mutual information. Neural computing and applications 24, 175–186 (2014). [18] Farahani, G. Feature selection based on cross-correlation for the intrusion detection system. Security and Communication Networks 1–17 (2020). Giraud, C. Introduction to High-Dimensional Statistics (Chapman and Hall/CRC, 2015). [11] Cawley, G. C. & Talbot, N. L. C. On over-fitting in model selection and subsequent selection bias in performance evaluation. Journal of Machine Learning Research 11, 2079–2107 (2010). URL http://jmlr.org/papers/v11/cawley10a.html. [12] Dua, D. & Graff, C. Uci machine learning repository (2017). URL http://archive.ics.uci.edu/ml. [13] Sejnowski, T. & Gorman, R. Connectionist Bench (Sonar, Mines vs. Rocks). UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5T01Q. [14] Rafiei, M. Residential Building Data Set. UCI Machine Learning Repository (2018). DOI: https://doi.org/10.24432/C5S896. [15] Ke, G. et al. Guyon, I. et al. (eds) Lightgbm: A highly efficient gradient boosting decision tree. (eds Guyon, I. et al.) Advances in Neural Information Processing Systems, Vol. 30 (Curran Associates, Inc., 2017). URL https://proceedings.neurips.cc/paper_files/paper/2017/file/6449f44a102fde848669bdd9eb6b76fa-Paper.pdf. [16] Goodfellow, I. J., Bengio, Y. & Courville, A. Deep Learning (MIT Press, Cambridge, MA, USA, 2016). http://www.deeplearningbook.org. [17] Vergara, J. R. & Estévez, P. A. A review of feature selection methods based on mutual information. Neural computing and applications 24, 175–186 (2014). [18] Farahani, G. Feature selection based on cross-correlation for the intrusion detection system. Security and Communication Networks 1–17 (2020). Cawley, G. C. & Talbot, N. L. C. 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URL https://proceedings.neurips.cc/paper_files/paper/2017/file/6449f44a102fde848669bdd9eb6b76fa-Paper.pdf. [16] Goodfellow, I. J., Bengio, Y. & Courville, A. Deep Learning (MIT Press, Cambridge, MA, USA, 2016). http://www.deeplearningbook.org. [17] Vergara, J. R. & Estévez, P. A. A review of feature selection methods based on mutual information. Neural computing and applications 24, 175–186 (2014). [18] Farahani, G. Feature selection based on cross-correlation for the intrusion detection system. Security and Communication Networks 1–17 (2020). Sejnowski, T. & Gorman, R. Connectionist Bench (Sonar, Mines vs. Rocks). UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5T01Q. [14] Rafiei, M. Residential Building Data Set. UCI Machine Learning Repository (2018). DOI: https://doi.org/10.24432/C5S896. [15] Ke, G. et al. Guyon, I. et al. (eds) Lightgbm: A highly efficient gradient boosting decision tree. (eds Guyon, I. et al.) Advances in Neural Information Processing Systems, Vol. 30 (Curran Associates, Inc., 2017). URL https://proceedings.neurips.cc/paper_files/paper/2017/file/6449f44a102fde848669bdd9eb6b76fa-Paper.pdf. [16] Goodfellow, I. J., Bengio, Y. & Courville, A. Deep Learning (MIT Press, Cambridge, MA, USA, 2016). http://www.deeplearningbook.org. [17] Vergara, J. R. & Estévez, P. A. A review of feature selection methods based on mutual information. Neural computing and applications 24, 175–186 (2014). [18] Farahani, G. Feature selection based on cross-correlation for the intrusion detection system. Security and Communication Networks 1–17 (2020). Rafiei, M. Residential Building Data Set. UCI Machine Learning Repository (2018). DOI: https://doi.org/10.24432/C5S896. [15] Ke, G. et al. Guyon, I. et al. (eds) Lightgbm: A highly efficient gradient boosting decision tree. (eds Guyon, I. et al.) Advances in Neural Information Processing Systems, Vol. 30 (Curran Associates, Inc., 2017). URL https://proceedings.neurips.cc/paper_files/paper/2017/file/6449f44a102fde848669bdd9eb6b76fa-Paper.pdf. [16] Goodfellow, I. J., Bengio, Y. & Courville, A. Deep Learning (MIT Press, Cambridge, MA, USA, 2016). http://www.deeplearningbook.org. [17] Vergara, J. R. & Estévez, P. A. A review of feature selection methods based on mutual information. Neural computing and applications 24, 175–186 (2014). [18] Farahani, G. Feature selection based on cross-correlation for the intrusion detection system. Security and Communication Networks 1–17 (2020). Ke, G. et al. Guyon, I. et al. (eds) Lightgbm: A highly efficient gradient boosting decision tree. (eds Guyon, I. et al.) Advances in Neural Information Processing Systems, Vol. 30 (Curran Associates, Inc., 2017). URL https://proceedings.neurips.cc/paper_files/paper/2017/file/6449f44a102fde848669bdd9eb6b76fa-Paper.pdf. [16] Goodfellow, I. J., Bengio, Y. & Courville, A. 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[13] Sejnowski, T. & Gorman, R. Connectionist Bench (Sonar, Mines vs. Rocks). UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5T01Q. [14] Rafiei, M. Residential Building Data Set. UCI Machine Learning Repository (2018). DOI: https://doi.org/10.24432/C5S896. [15] Ke, G. et al. Guyon, I. et al. (eds) Lightgbm: A highly efficient gradient boosting decision tree. (eds Guyon, I. et al.) Advances in Neural Information Processing Systems, Vol. 30 (Curran Associates, Inc., 2017). URL https://proceedings.neurips.cc/paper_files/paper/2017/file/6449f44a102fde848669bdd9eb6b76fa-Paper.pdf. [16] Goodfellow, I. J., Bengio, Y. & Courville, A. Deep Learning (MIT Press, Cambridge, MA, USA, 2016). http://www.deeplearningbook.org. [17] Vergara, J. R. & Estévez, P. A. A review of feature selection methods based on mutual information. Neural computing and applications 24, 175–186 (2014). [18] Farahani, G. Feature selection based on cross-correlation for the intrusion detection system. 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[16] Goodfellow, I. J., Bengio, Y. & Courville, A. Deep Learning (MIT Press, Cambridge, MA, USA, 2016). http://www.deeplearningbook.org. [17] Vergara, J. R. & Estévez, P. A. A review of feature selection methods based on mutual information. Neural computing and applications 24, 175–186 (2014). [18] Farahani, G. Feature selection based on cross-correlation for the intrusion detection system. Security and Communication Networks 1–17 (2020). Dua, D. & Graff, C. Uci machine learning repository (2017). URL http://archive.ics.uci.edu/ml. [13] Sejnowski, T. & Gorman, R. Connectionist Bench (Sonar, Mines vs. Rocks). UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5T01Q. [14] Rafiei, M. Residential Building Data Set. UCI Machine Learning Repository (2018). DOI: https://doi.org/10.24432/C5S896. [15] Ke, G. et al. Guyon, I. et al. (eds) Lightgbm: A highly efficient gradient boosting decision tree. (eds Guyon, I. et al.) Advances in Neural Information Processing Systems, Vol. 30 (Curran Associates, Inc., 2017). URL https://proceedings.neurips.cc/paper_files/paper/2017/file/6449f44a102fde848669bdd9eb6b76fa-Paper.pdf. [16] Goodfellow, I. J., Bengio, Y. & Courville, A. Deep Learning (MIT Press, Cambridge, MA, USA, 2016). http://www.deeplearningbook.org. [17] Vergara, J. R. & Estévez, P. A. A review of feature selection methods based on mutual information. Neural computing and applications 24, 175–186 (2014). [18] Farahani, G. Feature selection based on cross-correlation for the intrusion detection system. Security and Communication Networks 1–17 (2020). Sejnowski, T. & Gorman, R. Connectionist Bench (Sonar, Mines vs. Rocks). UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5T01Q. [14] Rafiei, M. Residential Building Data Set. UCI Machine Learning Repository (2018). DOI: https://doi.org/10.24432/C5S896. [15] Ke, G. et al. Guyon, I. et al. (eds) Lightgbm: A highly efficient gradient boosting decision tree. (eds Guyon, I. et al.) Advances in Neural Information Processing Systems, Vol. 30 (Curran Associates, Inc., 2017). URL https://proceedings.neurips.cc/paper_files/paper/2017/file/6449f44a102fde848669bdd9eb6b76fa-Paper.pdf. [16] Goodfellow, I. J., Bengio, Y. & Courville, A. Deep Learning (MIT Press, Cambridge, MA, USA, 2016). http://www.deeplearningbook.org. [17] Vergara, J. R. & Estévez, P. A. A review of feature selection methods based on mutual information. Neural computing and applications 24, 175–186 (2014). [18] Farahani, G. Feature selection based on cross-correlation for the intrusion detection system. Security and Communication Networks 1–17 (2020). Rafiei, M. Residential Building Data Set. UCI Machine Learning Repository (2018). DOI: https://doi.org/10.24432/C5S896. [15] Ke, G. et al. Guyon, I. et al. (eds) Lightgbm: A highly efficient gradient boosting decision tree. (eds Guyon, I. et al.) Advances in Neural Information Processing Systems, Vol. 30 (Curran Associates, Inc., 2017). URL https://proceedings.neurips.cc/paper_files/paper/2017/file/6449f44a102fde848669bdd9eb6b76fa-Paper.pdf. [16] Goodfellow, I. J., Bengio, Y. & Courville, A. Deep Learning (MIT Press, Cambridge, MA, USA, 2016). http://www.deeplearningbook.org. [17] Vergara, J. R. & Estévez, P. A. A review of feature selection methods based on mutual information. Neural computing and applications 24, 175–186 (2014). [18] Farahani, G. Feature selection based on cross-correlation for the intrusion detection system. Security and Communication Networks 1–17 (2020). Ke, G. et al. Guyon, I. et al. (eds) Lightgbm: A highly efficient gradient boosting decision tree. (eds Guyon, I. et al.) Advances in Neural Information Processing Systems, Vol. 30 (Curran Associates, Inc., 2017). URL https://proceedings.neurips.cc/paper_files/paper/2017/file/6449f44a102fde848669bdd9eb6b76fa-Paper.pdf. [16] Goodfellow, I. J., Bengio, Y. & Courville, A. Deep Learning (MIT Press, Cambridge, MA, USA, 2016). http://www.deeplearningbook.org. [17] Vergara, J. R. & Estévez, P. A. A review of feature selection methods based on mutual information. Neural computing and applications 24, 175–186 (2014). [18] Farahani, G. Feature selection based on cross-correlation for the intrusion detection system. Security and Communication Networks 1–17 (2020). Goodfellow, I. J., Bengio, Y. & Courville, A. Deep Learning (MIT Press, Cambridge, MA, USA, 2016). http://www.deeplearningbook.org. [17] Vergara, J. R. & Estévez, P. A. A review of feature selection methods based on mutual information. Neural computing and applications 24, 175–186 (2014). [18] Farahani, G. Feature selection based on cross-correlation for the intrusion detection system. Security and Communication Networks 1–17 (2020). Vergara, J. R. & Estévez, P. A. A review of feature selection methods based on mutual information. 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  10. Giraud, C. Introduction to High-Dimensional Statistics (Chapman and Hall/CRC, 2015). [11] Cawley, G. C. & Talbot, N. L. C. On over-fitting in model selection and subsequent selection bias in performance evaluation. Journal of Machine Learning Research 11, 2079–2107 (2010). URL http://jmlr.org/papers/v11/cawley10a.html. [12] Dua, D. & Graff, C. Uci machine learning repository (2017). URL http://archive.ics.uci.edu/ml. [13] Sejnowski, T. & Gorman, R. Connectionist Bench (Sonar, Mines vs. Rocks). UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5T01Q. [14] Rafiei, M. Residential Building Data Set. UCI Machine Learning Repository (2018). DOI: https://doi.org/10.24432/C5S896. [15] Ke, G. et al. Guyon, I. et al. (eds) Lightgbm: A highly efficient gradient boosting decision tree. (eds Guyon, I. et al.) Advances in Neural Information Processing Systems, Vol. 30 (Curran Associates, Inc., 2017). URL https://proceedings.neurips.cc/paper_files/paper/2017/file/6449f44a102fde848669bdd9eb6b76fa-Paper.pdf. [16] Goodfellow, I. J., Bengio, Y. & Courville, A. Deep Learning (MIT Press, Cambridge, MA, USA, 2016). http://www.deeplearningbook.org. [17] Vergara, J. R. & Estévez, P. A. A review of feature selection methods based on mutual information. Neural computing and applications 24, 175–186 (2014). [18] Farahani, G. Feature selection based on cross-correlation for the intrusion detection system. Security and Communication Networks 1–17 (2020). Cawley, G. C. & Talbot, N. L. C. On over-fitting in model selection and subsequent selection bias in performance evaluation. Journal of Machine Learning Research 11, 2079–2107 (2010). URL http://jmlr.org/papers/v11/cawley10a.html. [12] Dua, D. & Graff, C. Uci machine learning repository (2017). URL http://archive.ics.uci.edu/ml. [13] Sejnowski, T. & Gorman, R. Connectionist Bench (Sonar, Mines vs. Rocks). UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5T01Q. [14] Rafiei, M. Residential Building Data Set. UCI Machine Learning Repository (2018). DOI: https://doi.org/10.24432/C5S896. [15] Ke, G. et al. Guyon, I. et al. (eds) Lightgbm: A highly efficient gradient boosting decision tree. (eds Guyon, I. et al.) Advances in Neural Information Processing Systems, Vol. 30 (Curran Associates, Inc., 2017). URL https://proceedings.neurips.cc/paper_files/paper/2017/file/6449f44a102fde848669bdd9eb6b76fa-Paper.pdf. [16] Goodfellow, I. J., Bengio, Y. & Courville, A. Deep Learning (MIT Press, Cambridge, MA, USA, 2016). http://www.deeplearningbook.org. [17] Vergara, J. R. & Estévez, P. A. A review of feature selection methods based on mutual information. Neural computing and applications 24, 175–186 (2014). [18] Farahani, G. Feature selection based on cross-correlation for the intrusion detection system. Security and Communication Networks 1–17 (2020). Dua, D. & Graff, C. Uci machine learning repository (2017). URL http://archive.ics.uci.edu/ml. [13] Sejnowski, T. & Gorman, R. Connectionist Bench (Sonar, Mines vs. Rocks). UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5T01Q. [14] Rafiei, M. Residential Building Data Set. UCI Machine Learning Repository (2018). DOI: https://doi.org/10.24432/C5S896. [15] Ke, G. et al. Guyon, I. et al. (eds) Lightgbm: A highly efficient gradient boosting decision tree. (eds Guyon, I. et al.) Advances in Neural Information Processing Systems, Vol. 30 (Curran Associates, Inc., 2017). URL https://proceedings.neurips.cc/paper_files/paper/2017/file/6449f44a102fde848669bdd9eb6b76fa-Paper.pdf. [16] Goodfellow, I. J., Bengio, Y. & Courville, A. Deep Learning (MIT Press, Cambridge, MA, USA, 2016). http://www.deeplearningbook.org. [17] Vergara, J. R. & Estévez, P. A. A review of feature selection methods based on mutual information. Neural computing and applications 24, 175–186 (2014). [18] Farahani, G. Feature selection based on cross-correlation for the intrusion detection system. Security and Communication Networks 1–17 (2020). Sejnowski, T. & Gorman, R. Connectionist Bench (Sonar, Mines vs. Rocks). UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5T01Q. [14] Rafiei, M. Residential Building Data Set. UCI Machine Learning Repository (2018). DOI: https://doi.org/10.24432/C5S896. [15] Ke, G. et al. Guyon, I. et al. (eds) Lightgbm: A highly efficient gradient boosting decision tree. (eds Guyon, I. et al.) Advances in Neural Information Processing Systems, Vol. 30 (Curran Associates, Inc., 2017). URL https://proceedings.neurips.cc/paper_files/paper/2017/file/6449f44a102fde848669bdd9eb6b76fa-Paper.pdf. [16] Goodfellow, I. J., Bengio, Y. & Courville, A. Deep Learning (MIT Press, Cambridge, MA, USA, 2016). http://www.deeplearningbook.org. [17] Vergara, J. R. & Estévez, P. A. A review of feature selection methods based on mutual information. Neural computing and applications 24, 175–186 (2014). [18] Farahani, G. Feature selection based on cross-correlation for the intrusion detection system. Security and Communication Networks 1–17 (2020). Rafiei, M. Residential Building Data Set. UCI Machine Learning Repository (2018). DOI: https://doi.org/10.24432/C5S896. [15] Ke, G. et al. Guyon, I. et al. (eds) Lightgbm: A highly efficient gradient boosting decision tree. (eds Guyon, I. et al.) Advances in Neural Information Processing Systems, Vol. 30 (Curran Associates, Inc., 2017). URL https://proceedings.neurips.cc/paper_files/paper/2017/file/6449f44a102fde848669bdd9eb6b76fa-Paper.pdf. [16] Goodfellow, I. J., Bengio, Y. & Courville, A. Deep Learning (MIT Press, Cambridge, MA, USA, 2016). http://www.deeplearningbook.org. [17] Vergara, J. R. & Estévez, P. A. A review of feature selection methods based on mutual information. Neural computing and applications 24, 175–186 (2014). [18] Farahani, G. Feature selection based on cross-correlation for the intrusion detection system. Security and Communication Networks 1–17 (2020). Ke, G. et al. Guyon, I. et al. (eds) Lightgbm: A highly efficient gradient boosting decision tree. (eds Guyon, I. et al.) Advances in Neural Information Processing Systems, Vol. 30 (Curran Associates, Inc., 2017). URL https://proceedings.neurips.cc/paper_files/paper/2017/file/6449f44a102fde848669bdd9eb6b76fa-Paper.pdf. [16] Goodfellow, I. J., Bengio, Y. & Courville, A. Deep Learning (MIT Press, Cambridge, MA, USA, 2016). http://www.deeplearningbook.org. [17] Vergara, J. R. & Estévez, P. A. A review of feature selection methods based on mutual information. Neural computing and applications 24, 175–186 (2014). [18] Farahani, G. Feature selection based on cross-correlation for the intrusion detection system. Security and Communication Networks 1–17 (2020). Goodfellow, I. J., Bengio, Y. & Courville, A. Deep Learning (MIT Press, Cambridge, MA, USA, 2016). http://www.deeplearningbook.org. [17] Vergara, J. R. & Estévez, P. A. A review of feature selection methods based on mutual information. Neural computing and applications 24, 175–186 (2014). [18] Farahani, G. Feature selection based on cross-correlation for the intrusion detection system. Security and Communication Networks 1–17 (2020). Vergara, J. R. & Estévez, P. A. A review of feature selection methods based on mutual information. Neural computing and applications 24, 175–186 (2014). [18] Farahani, G. Feature selection based on cross-correlation for the intrusion detection system. Security and Communication Networks 1–17 (2020). Farahani, G. Feature selection based on cross-correlation for the intrusion detection system. Security and Communication Networks 1–17 (2020).
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  14. Rafiei, M. Residential Building Data Set. UCI Machine Learning Repository (2018). DOI: https://doi.org/10.24432/C5S896. [15] Ke, G. et al. Guyon, I. et al. (eds) Lightgbm: A highly efficient gradient boosting decision tree. (eds Guyon, I. et al.) Advances in Neural Information Processing Systems, Vol. 30 (Curran Associates, Inc., 2017). URL https://proceedings.neurips.cc/paper_files/paper/2017/file/6449f44a102fde848669bdd9eb6b76fa-Paper.pdf. [16] Goodfellow, I. J., Bengio, Y. & Courville, A. Deep Learning (MIT Press, Cambridge, MA, USA, 2016). http://www.deeplearningbook.org. [17] Vergara, J. R. & Estévez, P. A. A review of feature selection methods based on mutual information. Neural computing and applications 24, 175–186 (2014). [18] Farahani, G. Feature selection based on cross-correlation for the intrusion detection system. Security and Communication Networks 1–17 (2020). Ke, G. et al. Guyon, I. et al. (eds) Lightgbm: A highly efficient gradient boosting decision tree. (eds Guyon, I. et al.) Advances in Neural Information Processing Systems, Vol. 30 (Curran Associates, Inc., 2017). URL https://proceedings.neurips.cc/paper_files/paper/2017/file/6449f44a102fde848669bdd9eb6b76fa-Paper.pdf. [16] Goodfellow, I. J., Bengio, Y. & Courville, A. Deep Learning (MIT Press, Cambridge, MA, USA, 2016). http://www.deeplearningbook.org. [17] Vergara, J. R. & Estévez, P. A. A review of feature selection methods based on mutual information. Neural computing and applications 24, 175–186 (2014). [18] Farahani, G. Feature selection based on cross-correlation for the intrusion detection system. Security and Communication Networks 1–17 (2020). Goodfellow, I. J., Bengio, Y. & Courville, A. Deep Learning (MIT Press, Cambridge, MA, USA, 2016). http://www.deeplearningbook.org. [17] Vergara, J. R. & Estévez, P. A. A review of feature selection methods based on mutual information. Neural computing and applications 24, 175–186 (2014). [18] Farahani, G. Feature selection based on cross-correlation for the intrusion detection system. Security and Communication Networks 1–17 (2020). Vergara, J. R. & Estévez, P. A. A review of feature selection methods based on mutual information. Neural computing and applications 24, 175–186 (2014). [18] Farahani, G. Feature selection based on cross-correlation for the intrusion detection system. Security and Communication Networks 1–17 (2020). Farahani, G. Feature selection based on cross-correlation for the intrusion detection system. Security and Communication Networks 1–17 (2020).
  15. Ke, G. et al. Guyon, I. et al. (eds) Lightgbm: A highly efficient gradient boosting decision tree. (eds Guyon, I. et al.) Advances in Neural Information Processing Systems, Vol. 30 (Curran Associates, Inc., 2017). URL https://proceedings.neurips.cc/paper_files/paper/2017/file/6449f44a102fde848669bdd9eb6b76fa-Paper.pdf. [16] Goodfellow, I. J., Bengio, Y. & Courville, A. Deep Learning (MIT Press, Cambridge, MA, USA, 2016). http://www.deeplearningbook.org. [17] Vergara, J. R. & Estévez, P. A. A review of feature selection methods based on mutual information. Neural computing and applications 24, 175–186 (2014). [18] Farahani, G. Feature selection based on cross-correlation for the intrusion detection system. Security and Communication Networks 1–17 (2020). Goodfellow, I. J., Bengio, Y. & Courville, A. Deep Learning (MIT Press, Cambridge, MA, USA, 2016). http://www.deeplearningbook.org. [17] Vergara, J. R. & Estévez, P. A. A review of feature selection methods based on mutual information. Neural computing and applications 24, 175–186 (2014). [18] Farahani, G. Feature selection based on cross-correlation for the intrusion detection system. Security and Communication Networks 1–17 (2020). Vergara, J. R. & Estévez, P. A. A review of feature selection methods based on mutual information. Neural computing and applications 24, 175–186 (2014). [18] Farahani, G. Feature selection based on cross-correlation for the intrusion detection system. Security and Communication Networks 1–17 (2020). Farahani, G. Feature selection based on cross-correlation for the intrusion detection system. Security and Communication Networks 1–17 (2020).
  16. Deep Learning (MIT Press, Cambridge, MA, USA, 2016). http://www.deeplearningbook.org. [17] Vergara, J. R. & Estévez, P. A. A review of feature selection methods based on mutual information. Neural computing and applications 24, 175–186 (2014). [18] Farahani, G. Feature selection based on cross-correlation for the intrusion detection system. Security and Communication Networks 1–17 (2020). Vergara, J. R. & Estévez, P. A. A review of feature selection methods based on mutual information. Neural computing and applications 24, 175–186 (2014). [18] Farahani, G. Feature selection based on cross-correlation for the intrusion detection system. Security and Communication Networks 1–17 (2020). Farahani, G. Feature selection based on cross-correlation for the intrusion detection system. Security and Communication Networks 1–17 (2020).
  17. A review of feature selection methods based on mutual information. Neural computing and applications 24, 175–186 (2014). [18] Farahani, G. Feature selection based on cross-correlation for the intrusion detection system. Security and Communication Networks 1–17 (2020). Farahani, G. Feature selection based on cross-correlation for the intrusion detection system. Security and Communication Networks 1–17 (2020).
  18. Farahani, G. Feature selection based on cross-correlation for the intrusion detection system. Security and Communication Networks 1–17 (2020).

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