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A New Fuzzy and Correlation Based Feature Selection Method for Multiclass Problems

Soheila Barchinezhad, Mahdi Eftekhari


Feature selection is one of the most important subjects in machine learning and pattern recognition. The main idea in feature selection algorithms is selecting a subset of features which does not include irrelevant and redundant features. In this paper a feature selection algorithm using genetic algorithm and fuzzy sets theory is proposed which we call fuzzy and correlation based feature selection - FCFS. We apply four fuzzy systems to obtain the fitness function in genetic algorithm. This filter method selects a low size feature subset so that the relevancy of each feature with the target is maximized and the redundancy among the selected features is minimized. Relevancy and redundancy are calculated based on Pearson’s correlation coefficient criterion. Several experiments are provided to demonstrate the effectiveness of the idea in terms of the classification accuracy and the number of selected features. Some statistical tests are also used to show the significant differences between the proposed method and the other methods.


Feature Selection, Fuzzy, Genetic Algorithm, Subset Selection, Filter.

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