International Journal of Computer Science & Network Security

International Journal of Computer Science & Network Security (국제컴퓨터통신보호논문지학회)
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Centroid and Nearest Neighbor based Class Imbalance Reduction with Relevant Feature Selection using Ant Colony Optimization for Software Defect Prediction

  • B., Kiran Kumar (Department of Information Technology, Kakatiya Institute of Technology & Science) ;
  • Gyani, Jayadev (Department of CS, College of Computer and Information Sciences, Majmaah University) ;
  • Y., Bhavani (Department of Information Technology, Kakatiya Institute of Technology & Science) ;
  • P., Ganesh Reddy (Department of Information Technology, Kakatiya Institute of Technology & Science) ;
  • T, Nagasai Anjani Kumar (Department of Information Technology, Kakatiya Institute of Technology & Science)
  • Received : 2022.10.05
  • Published : 2022.10.30
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Abstract

Nowadays software defect prediction (SDP) is most active research going on in software engineering. Early detection of defects lowers the cost of the software and also improves reliability. Machine learning techniques are widely used to create SDP models based on programming measures. The majority of defect prediction models in the literature have problems with class imbalance and high dimensionality. In this paper, we proposed Centroid and Nearest Neighbor based Class Imbalance Reduction (CNNCIR) technique that considers dataset distribution characteristics to generate symmetry between defective and non-defective records in imbalanced datasets. The proposed approach is compared with SMOTE (Synthetic Minority Oversampling Technique). The high-dimensionality problem is addressed using Ant Colony Optimization (ACO) technique by choosing relevant features. We used nine different classifiers to analyze six open-source software defect datasets from the PROMISE repository and seven performance measures are used to evaluate them. The results of the proposed CNNCIR method with ACO based feature selection reveals that it outperforms SMOTE in the majority of cases.

Keywords

Acknowledgement

The authors would like to thank Deanship of Scientific Research at Majmaah University for supporting this work under Project Number No. xxxx. The author is also thankful to the anonymous reviewers for their useful comments.

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