A Cluster-Based Hybrid Feature Selection Method for Defect Prediction

Abstract

Machine learning is an effective method for software defect prediction. The performance of learning models can be affected by irrelative and redundant features. Feature selection techniques select a subset of most impactful relevant features that will result in higher accuracy and efficiency of models. This paper proposed a Cluster-based Hybrid Feature Selection method (CHIFS) for software defect prediction. A spectral cluster-based Feature Quality coefficient (FQ) was defined as a comprehensive measurement of feature relevance and redundancy. The final feature subset was iteratively selected from feature sequence ranked by FQ. The proposed CHIFS method was validated in the experiments using 3 classifiers with 15 open datasets from Promise Repository. Experimental results showed that the CHIFS method performed better than traditional methods in terms of accuracy and efficiency on a wide range of datasets.