Supervised feature subset selection using extended fuzzy absolute information measure for handling different discretized datasets

Abstract

Abstract Feature subset selection is applied in wide range of domains like biometrics, chemistry, text processing, pattern recognition, speech processing and vision. Most of the prior work in biometrics and other applications have emphasized the importance of feature extraction and classification. However, the critical issue of examining the usefulness of extracted features has been largely ignored. But feature subset selection helps to identify and remove much of the irrelevant and redundant features. It reduces the dimensions of datasets. So it avoids the problems faced in feature extraction. The proposed Extended Fuzzy Absolute Information Measure (EFAIM) is applied to select feature subsets by focusing on boundary samples. The proposed method can select feature subsets with the minimum number of features. The experimental results with UCI datasets show that the proposed method is effective and efficient in selecting subset with minimum number of features than fuzzy entropy measure. This experiment shows that among the given number of features in the datasets, a small relevant subset of features is only selected for feature subset. Thus the selected subset of features is necessary in practice for building an accurate result.