Cost-sensitive feature selection with application in software defect prediction

Linsong Miao, Mingxia Liu, Daoqiang Zhang

Abstract: In many real-world applications, different mis-classification errors will cause different costs. However, cost-sensitive learning only applied in classification phase and not in the feature selection phase to address this problem. In this paper, we study cost-sensitive feature selection and propose a framework which incorporates a cost matrix into traditional feature selection methods. And we developed three corresponding methods, namely, Cost-Sensitive Variance Score (CSVS), Cost-Sensitive Laplacian Score (CSLS), Cost-Sensitive Constraint Score (CSCS). Experiments on real software defect prediction benchmark data sets demonstrate that cost-sensitive feature selection methods are more efficacy than traditional ones in reducing the total cost.