Improved Hybrid Sampling Strategy for Software Defect Prediction of Imbalanced Data Distribution

Abstract

Software defect prediction using data mining techniques is one of the best practices for finding defective modules. The existing classification techniques can be used for efficient knowledge discovery on normal datasets. Most of the real-world data sources are biased towards any one of the classes. This type of data source is known as class imbalance or skewed data sources. The defect prediction rate for the class imbalance datasets reduces with the increases in the class imbalance nature. To handle such type of datasets, an approach with specific designing technique is required for improved performance. In this chapter, the authors propose an algorithm known as improved integrated sampling strategy (IISS) for improved performance using noisy removal strategy for software defect prediction. The experimental analysis conducted on skewed software defect prediction datasets provides the results that IISS algorithm have performed well when compared with C4.5, C4.5+Balance, RF, and RF+Balance algorithms with various class imbalance evaluation measures.