Performance Comparison of Feature Selection Methods

Abstract

Feature Subset Selection is an essential pre-processing task in Data Mining. Feature selection process refers to choosing subset of attributes from the set of original attributes. This technique attempts to identify and remove as much irrelevant and redundant information as possible. In this paper, a new feature subset selection algorithm based on conditional mutual information approach is proposed to select the effective feature subset. The effectiveness of the proposed algorithm is evaluated by comparing with the other well-known existing feature selection algorithms using standard datasets from UC Iravine and WEKA (Waikato Environment for Knowledge Analysis). The performance of the proposed algorithm is evaluated by multi-criteria that take into account not only the classification accuracy but also number of selected features.