Feature Selection Method Based on Weighted Mutual Information for Imbalanced Data

Abstract

The class imbalance problem has negative effects on the performance of feature selection in imbalanced data. Traditional feature selection algorithms always study on the balanced class distribution of the data and improve the overall classification accuracy for the optimization goal, which tends to be overwhelmed by the large classes, ignoring the small ones. This paper proposes a novel feature selection method based on the weighted mutual information (WMI) for the imbalanced data, defined as WMI algorithm. The WMI algorithm assigns different weights to the samples based on the fuzzy c-means (FCM) clustering algorithm and then calculates the mutual information based on the weight of each sample. This paper used the AUC as the evaluation criterion of the selected feature. At last, four unbalanced datasets from NASA software defect datasets are used to validate the proposed approach. Experimental results show that the proposed method achieves higher prediction accuracy of both minority class and majority class.