Feature Selection and Discretization based on Mutual Information

Abstract

Feature selection and discretization have been considered to be an important research topic in the field of pattern recognition and data mining. However, addressing both these issues at a time is rarely discussed in the existing research. In this paper, these issues have been addressed by developing a heuristic namely discretization and selection of features based on mutual information (DSM). Experimental results on 15 datasets show that the proposed DSM outperforms a number of state-of-the-art feature selection or discretization algorithms. On average, its accuracy surpasses that of the best performing state-of-the-art algorithms by 5% using Support Vector Machine. Moreover, for datasets with a large number of features, it shows promising accuracies even with far less number of features than the other competing algorithms.