Feature Selection and Discretization based on Mutual Information
Sadia Sharmin, Amin Ahsan Ali, Muhammad Asif Hossain Khan, Mohammad Shoyaib · 2017
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.