A Multi-Objective Feature Selection Method based on the Conditional Mutual Information and Pareto Set Theory

Feature selection is the process of selecting a subset of features among the set of primary features, so that, by removing the redundant and irrelevant features, the accuracy of the classification increases. Because of the low computational complexity, scalability in term of data dimensions and independence of any classifier, filter selection methods are very important. But one of the weaknesses of these methods is the lack of information about the interaction and communication between the features which leads to select redundant and irrelevant features. Selection of redundant and irrelevant features is due to the inappropriate selection of an objective function which estimates the significance and redundancy of the features. In this paper, a nonlinear filter feature selection method, based on conditional mutual information and Pareto set is presented and to prove the efficiency of it a series of experiments are performed on twelve widely used datasets. According to the results, the proposed method is more accurate than a number of recently feature selection methods.