Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
156 tokens/sec
GPT-4o
7 tokens/sec
Gemini 2.5 Pro Pro
45 tokens/sec
o3 Pro
4 tokens/sec
GPT-4.1 Pro
38 tokens/sec
DeepSeek R1 via Azure Pro
28 tokens/sec
2000 character limit reached

Attribute reduction algorithm of rough sets based on spatial optimization (2405.09292v1)

Published 15 May 2024 in cs.AI

Abstract: Rough set is one of the important methods for rule acquisition and attribute reduction. The current goal of rough set attribute reduction focuses more on minimizing the number of reduced attributes, but ignores the spatial similarity between reduced and decision attributes, which may lead to problems such as increased number of rules and limited generality. In this paper, a rough set attribute reduction algorithm based on spatial optimization is proposed. By introducing the concept of spatial similarity, to find the reduction with the highest spatial similarity, so that the spatial similarity between reduction and decision attributes is higher, and more concise and widespread rules are obtained. In addition, a comparative experiment with the traditional rough set attribute reduction algorithms is designed to prove the effectiveness of the rough set attribute reduction algorithm based on spatial optimization, which has made significant improvements on many datasets.

Definition Search Book Streamline Icon: https://streamlinehq.com
References (24)
  1. Pawlak.Z and Janusz, “Rough sets: Theoretical aspects of reasoning about data,” Control Engineering Practice, p. 741–742, May 1996.
  2. M. Kryszkiewicz, “Rough set approach to incomplete information systems,” Information sciences, vol. 112, no. 1-4, pp. 39–49, 1998.
  3. M. Kryszkiewicz, “Rules in incomplete information systems,” Information sciences, vol. 113, no. 3-4, pp. 271–292, 1999.
  4. A. Chaudhuri, D. Samanta, and M. Sarma, “Two-stage approach to feature set optimization for unsupervised dataset with heterogeneous attributes,” Expert Systems with Applications, vol. 172, p. 114563, 2021.
  5. Q. Hu, L. Zhang, D. Chen, W. Pedrycz, and D. Yu, “Gaussian kernel based fuzzy rough sets: model, uncertainty measures and applications,” International Journal of Approximate Reasoning, vol. 51, no. 4, pp. 453–471, 2010.
  6. S. Greco, B. Matarazzo, and R. Slowinski, “Rough sets theory for multicriteria decision analysis,” European journal of operational research, vol. 129, no. 1, pp. 1–47, 2001.
  7. Z. Pawlak, S. K. M. Wong, W. Ziarko, et al., “Rough sets: probabilistic versus deterministic approach,” International Journal of Man-Machine Studies, vol. 29, no. 1, pp. 81–95, 1988.
  8. W. Ziarko, “Variable precision rough set model,” Journal of computer and system sciences, vol. 46, no. 1, pp. 39–59, 1993.
  9. Y. Yao and S. K. M. Wong, “A decision theoretic framework for approximating concepts,” International journal of man-machine studies, vol. 37, no. 6, pp. 793–809, 1992.
  10. Y. Qian, J. Liang, Y. Yao, and C. Dang, “Mgrs: A multi-granulation rough set,” Information sciences, vol. 180, no. 6, pp. 949–970, 2010.
  11. J. Stefanowski and A. Tsoukiàs, “On the extension of rough sets under incomplete information,” in New Directions in Rough Sets, Data Mining, and Granular-Soft Computing: 7th International Workshop, RSFDGrC’99, Yamaguchi, Japan, November 9-11, 1999. Proceedings 7, pp. 73–81, Springer, 1999.
  12. D.-Y. Ye and Z.-J. Chen, “A new discernibility matrix and the computation of a core,” Acta electronica sinica, vol. 30, no. 7, pp. 1086–1088, 2002.
  13. Q. Hu, H. Zhao, and D. Yu, “Efficient symbolic and numerical attribute reduction with neighborhood rough sets,” Pattern Recognition and Artificial Intelligence, vol. 21, no. 6, pp. 732–738, 2008.
  14. W. Shu and W. Qian, “An incremental approach to attribute reduction from dynamic incomplete decision systems in rough set theory,” Data & Knowledge Engineering, vol. 100, pp. 116–132, 2015.
  15. A. Skowron and C. Rauszer, The discernibility matrices and functions in information systems. Intelligent Decision Support. Dordrecht:Springer Netherlands, 1992.
  16. X. Hu and N. Cercone, “Learning in relational databases: a rough set approach,” Computational intelligence, vol. 11, no. 2, pp. 323–338, 1995.
  17. M. Duoqian and W. Jue, “Information-based algorithm for reduction of knowledge,” in 1997 IEEE International Conference on Intelligent Processing Systems (Cat. No. 97TH8335), vol. 2, (USA: IEEE Publisher), pp. 1155–1158, IEEE, 1997.
  18. R. Jensen and Q. Shen, “Fuzzy-rough data reduction with ant colony optimization,” Fuzzy sets and systems, vol. 149, no. 1, pp. 5–20, 2005.
  19. C. Wang, Y. Huang, M. Shao, Q. Hu, and D. Chen, “Feature selection based on neighborhood self-information,” IEEE Transactions on Cybernetics, vol. 50, no. 9, pp. 4031–4042, 2019.
  20. C. Wang, Y. Huang, W. Ding, and Z. Cao, “Attribute reduction with fuzzy rough self-information measures,” Information Sciences, vol. 549, pp. 68–86, 2021.
  21. C. Wang, Y. Wang, M. Shao, Y. Qian, and D. Chen, “Fuzzy rough attribute reduction for categorical data,” IEEE Transactions on Fuzzy Systems, vol. 28, no. 5, pp. 818–830, 2019.
  22. S. Xia, H. Zhang, W. Li, G. Wang, E. Giem, and Z. Chen, “Gbnrs: A novel rough set algorithm for fast adaptive attribute reduction in classification,” IEEE Transactions on Knowledge and Data Engineering, vol. 34, no. 3, pp. 1231–1242, 2020.
  23. B. Sang, H. Chen, L. Yang, T. Li, and W. Xu, “Incremental feature selection using a conditional entropy based on fuzzy dominance neighborhood rough sets,” IEEE Transactions on Fuzzy Systems, vol. 30, no. 6, pp. 1683–1697, 2021.
  24. A. A. Ewees, M. Abd Elaziz, R. M. Arafa, and R. M. Ghoniem, “Improved approach based on fuzzy rough set and sine-cosine algorithm: A case study on prediction of osteoporosis,” IEEE Access, vol. 8, pp. 203190–203202, 2020.

Summary

We haven't generated a summary for this paper yet.

Ai Sparkles Streamline Icon: https://streamlinehq.com Summarize Now