SMOTEFRIS-INFFC: Handling the challenge of borderline and noisy examples in imbalanced learning for software defect prediction

Author:

Bashir Kamal12,Li Tianrui1,Yohannese Chubato Wondaferaw1,Yahaya Mahama3

Affiliation:

1. School of Information Science and Technology, Southwest Jiaotong University, Chengdu, China

2. Department of Information Technology, College of Computer Science and Information Technology, Karary University, Omdurman, Sudan

3. School of Transport and Logistics Engineering, Southwest Jiaotong University, Chengdu, China

Publisher

IOS Press

Subject

Artificial Intelligence,General Engineering,Statistics and Probability

Reference36 articles.

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2. Benchmarking classification models for software defect prediction: A proposed framework and novel findings;Lessmann;IEEE Transactions on Software Engineering,2008

3. A systematic review of software fault prediction studies;Catal;Expert Systems with Applications,2009

4. SMOTE: synthetic minority over-sampling technique;Chawla;Journal of Artificial Intelligence Research,2002

5. Enhancing software defect prediction using supervised-learning based framework;Bashir;12th International Conference on Intelligent Systems and Knowledge Engineering (ISKE),2017

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