Supervised Feature Subset Selection Based On Extended Fuzzy Relative Information Measure For Boundary Samples
نویسندگان
چکیده
Feature subset selection is an essential preprocessing task in data mining. This paper presents a new method called Extended Fuzzy Relative Information Measure for Boundary Samples (EFRIMBS) for dealing with supervised feature subset selection. The proposed algorithm uses boundary samples instead of full set of samples. First, Discretization algorithms such as K-Means, Fuzzy C Means and Median as Initial Centroid of K-Means are applied to discretize numeric features to construct the membership functions of each fuzzy sets of a feature. Then the proposed EFRIMBS is applied to select feature subset focusing on boundary samples. J.D.Shie and S.M.Chen’s fuzzy entropy measure (FE) for feature subset selection is also applied with different discretization algorithms and the results are compared with the proposed algorithm. The FE based feature selection algorithm is efficient only for smaller datasets, the proposed method is very efficient for small and large datasets which is selecting minimum number of features for feature subset. The experimental results for UCI datasets shows that the proposed method produces better results when compared with the existing one.
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