Feature subset selection is an essential task in data mining. This paper presents a new method for dealing with supervised feature subset selection based on Modified Fuzzy Relative Information Measure (MFRIM). First, Discretization algorithm is applied to discretize numeric features to construct the membership functions of each fuzzy sets of a feature. Then the proposed MFRIM is applied to select the feature subset focusing on boundary samples. The proposed method can select feature subset with minimum number of features, which are relevant to get higher average classification accuracy for datasets. The experimental results with UCI datasets show that the proposed algorithm is effective and efficient in selecting subset with minimum number of features getting higher average classification accuracy than the consistency based feature subset selection method.
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