On fuzzy-rough attribute selection: Criteria of Max-Dependency, Max-Relevance, Min-Redundancy, and Max-Significance

Attribute selection is one of the important problems encountered in pattern recognition, machine learning, data mining, and bioinformatics. It refers to the problem of selecting those input attributes or features that are most effective to predict the sample categories. In this regard, rough set theory has been shown to be successful for selecting relevant and nonredundant attributes from a given data set. However, the classical rough sets are unable to handle real valued noisy features. This problem can be addressed by the fuzzy-rough sets, which are the generalization of classical rough sets. A feature selection method is presented here based on fuzzy-rough sets by maximizing both relevance and significance of the selected features. This paper also presents different feature evaluation criteria such as dependency, relevance, redundancy, and significance for attribute selection task using fuzzy-rough sets. The performance of different rough set models is compared with that of some existing feature evaluation indices based on the predictive accuracy of nearest neighbor rule, support vector machine, and decision tree. The effectiveness of the fuzzy-rough set based attribute selection method, along with a comparison with existing feature evaluation indices and different rough set models, is demonstrated on a set of benchmark and microarray gene expression data sets.

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► A feature selection method is presented in this paper based on fuzzy-rough sets.
► It maximizes both relevance and significance of the selected features.
► The paper also presents dependency, relevance, redundancy, and significance criteria for attribute selection task using fuzzy-rough sets.
► The performance of different rough set models is compared with that of some existing feature evaluation indices.
► The effectiveness of different approaches is demonstrated on benchmark and microarray data sets.