F. Wang, J. Ai and Z. Zou, “A Cluster-Based Hybrid Feature Selection Method for Defect Prediction,” 2019 IEEE 19th International Conference on Software Quality, Reliability and Security (QRS), 2019, pp. 1-9, doi: 10.1109/QRS.2019.00014.
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.