@inproceedings{199ea3403ae14022b60ceb3a6f31ef76,
title = "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.",
keywords = "defect prediction, feature selection, software network, spectral cluster",
author = "Fei Wang and Jun Ai and Zhuoliang Zou",
note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 19th IEEE International Conference on Software Quality, Reliability and Security, QRS 2019 ; Conference date: 22-07-2019 Through 26-07-2019",
year = "2019",
month = jul,
doi = "10.1109/QRS.2019.00014",
language = "英语",
series = "Proceedings - 19th IEEE International Conference on Software Quality, Reliability and Security, QRS 2019",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "1--9",
booktitle = "Proceedings - 19th IEEE International Conference on Software Quality, Reliability and Security, QRS 2019",
address = "美国",
}