Asymmetric Learning Based on Kernel Partial Least Squares for Software Defect Prediction

摘要

An asymmetric classifier based on kernel partial least squares is proposed for software defect prediction. This method improves the prediction performance on imbalanced data sets. The experimental results validate its effectiveness.