A systematic comparison of supervised classifiers (1311.0202v1)

Abstract: Pattern recognition techniques have been employed in a myriad of industrial, medical, commercial and academic applications. To tackle such a diversity of data, many techniques have been devised. However, despite the long tradition of pattern recognition research, there is no technique that yields the best classification in all scenarios. Therefore, the consideration of as many as possible techniques presents itself as an fundamental practice in applications aiming at high accuracy. Typical works comparing methods either emphasize the performance of a given algorithm in validation tests or systematically compare various algorithms, assuming that the practical use of these methods is done by experts. In many occasions, however, researchers have to deal with their practical classification tasks without an in-depth knowledge about the underlying mechanisms behind parameters. Actually, the adequate choice of classifiers and parameters alike in such practical circumstances constitutes a long-standing problem and is the subject of the current paper. We carried out a study on the performance of nine well-known classifiers implemented by the Weka framework and compared the dependence of the accuracy with their configuration parameter configurations. The analysis of performance with default parameters revealed that the k-nearest neighbors method exceeds by a large margin the other methods when high dimensional datasets are considered. When other configuration of parameters were allowed, we found that it is possible to improve the quality of SVM in more than 20% even if parameters are set randomly. Taken together, the investigation conducted in this paper suggests that, apart from the SVM implementation, Weka's default configuration of parameters provides an performance close the one achieved with the optimal configuration.

• The paper systematically compares the performance of various supervised classifiers in Weka on datasets of different dimensions, evaluating both default and varied parameter settings.
• Key findings indicate that kNN performs exceptionally well on high-dimensional data with default settings, while complex classifiers like SVM often require parameter tuning for optimal accuracy, showing significant improvements.
• The study provides practical guidance on selecting classifiers based on data characteristics and highlights the potential benefits of automated parameter optimization in machine learning frameworks.

A Systematic Comparison of Supervised Classifiers: An Expert Review

The paper "A Systematic Comparison of Supervised Classifiers" provides an empirical paper of the performance of various supervised classification algorithms implemented within the Weka software framework. The investigation is focused on comparing the accuracy of these classifiers when applied to datasets of varying dimensions, particularly considering default parameter settings versus random parameter configurations.

Evaluation of Classifiers with Default Settings

One of the overarching evaluations of the paper is how commonly used classifiers—such as Naive Bayes, k-Nearest Neighbors (kNN), Support Vector Machine (SVM), Random Forest, C4.5, and others—perform when employed with Weka's default parameter settings. It is highlighted that kNN performs exceptionally well in high-dimensional datasets (10 features), achieving an average accuracy of 94.28%, which was significant compared to others. In contrast, under the same conditions, the Bayesian Network exhibited a considerably lower performance, achieving an average accuracy of 56.87%.

For two-dimensional datasets, while the performance discrepancy amongst different classifiers was less stark, Naive Bayes exhibited the highest average accuracy. This disparity reflects the influence that the number of features has on classifier performance, emphasizing the need for careful selection of algorithms based on data characteristics.

Sensitivity to Parameter Variation

The paper further explores the impact of parameter tuning on classifier performance through a one-dimensional analysis where each parameter is varied individually with others set to default. The findings suggest that for most classifiers, default parameters suffice in delivering near-optimal performance. However, notable exceptions appear; for instance, adjusting the number of neighbors in kNN and kernel parameters in SVM can lead to significant accuracy improvements.

Multidimensional Parameter Exploration

The paper also investigates a multidimensional approach for assessing classifier performance, wherein parameters are randomly sampled to evaluate their effect collectively. Results from this section underscore that SVM can yield substantial improvements (up to 20.35% in high-dimensional data) when parameters are suitably configured, offering a stronger performance prospect than when default settings are used.

Practical and Theoretical Implications

The research delineates important practical implications, notably the efficacy of using kNN for high-dimensional datasets, which suggests that there may be less need for extensive parameter tuning in certain contexts. Conversely, for inherently complex methods like SVMs, this paper highlights the potential and necessity of parameter tuning to harness full classifier performance, which can be particularly beneficial in contexts with high feature dimensions.

From a theoretical perspective, the paper implicitly argues for more flexible optimization frameworks in machine learning tools that can adapt classifier parameters dynamically, based on dataset characteristics. This contributes to a broader discussion in machine learning on balancing ease-of-use with the flexibility required for optimal performance.

Future Perspectives

The paper’s insights on classifier performance suggest several avenues for future research, including exploring a more diverse set of artificial and real-world datasets to generalize findings. Moreover, the introduction of automated parameter optimization algorithms within frameworks like Weka could provide a practical tool for practitioners seeking high accuracy without intricate manual tuning efforts.

In conclusion, this systematic comparison underscores the critical aspect of feature dimensionality and parameter configuration in determining classifier performance, providing a detailed guide for practitioners and researchers who use Weka or similar machine learning tools for classification tasks.