Feature selection is one of the important research areas in pattern recognition. The aim of feature selection is to select those of informative features to improve the classifier's performance. In this paper, we propose a novel multi-objective algorithm based on mutual information for feature selection, called multi-objective mutual information (MOMI). The proposed method identifies a set of features with minimal redundancy and maximum relevancy with the target class. Several experiments are performed to evaluate the performance of MOMI compared to that of well-known and state-of-the-art feature selection methods over five benchmark datasets. The results show that in most cases MOMI achieves better classification performance than others.
