A Population based Feature Subset Selection Algorithm Guided by Fuzzy Feature Dependency

Springer Berlin Heidelberg
Publication Type:
Conference Proceeding
Advanced Machine Learning Technologies and Applications, 2012, pp. 430 - 438
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Population-based (or evolutionary) algorithms have been attracting an increased attention due to their powerful search capabilities. For the particular problem of feature selection, population-based methods aim to produce better or fitter future generations that contain more informative subsets of features. It is well-known that feature subset selection is a very challenging optimization problem, especially when dealing with datasets that contain large number of features. Most of the commonly used population-based feature selection methods use operators that do not take into account relationships between features to generate future subsets, which can have an impact on their capabilities to properly explore the search space. We present here a new populationbased feature selection method that utilize dependency between features to guide the search. In addition, a novel method for estimating dependency between feature pairs is proposed based on the concept of fuzzy entropy. Results obtained from datasets with various sizes indicate the superiority of the proposed method in comparison to some of the wellknown methods in the literature.
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