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dc.contributor.authorKwabena E. Benninen_US
dc.contributor.authorJacky Keungen_US
dc.contributor.authorPassakorn Phannachittaen_US
dc.contributor.authorAkito Mondenen_US
dc.contributor.authorSolomon Mensahen_US
dc.description.abstract© 2018 ACM. This study presents MAHAKIL, a novel and efficient synthetic over-sampling approach for software defect datasets that is based on the chromosomal theory of inheritance. Exploiting this theory, MAHAKIL interprets two distinct sub-classes as parents and generates a new instance that inherits different traits from each parent and contributes to the diversity within the data distribution. We extensively compare MAHAKIL with five other sampling approaches using 20 releases of defect datasets from the PROMISE repository and five prediction models. Our experiments indicate that MAHAKIL improves the prediction performance for all the models and achieves better and more significant pf values than the other oversampling approaches, based on robust statistical tests.en_US
dc.subjectComputer Scienceen_US
dc.titleMAHAKIL: Diversity based oversampling approach to alleviate the class imbalance issue in software defect predictionen_US
dc.typeConference Proceedingen_US
article.title.sourcetitleProceedings - International Conference on Software Engineeringen_US University of Hong Kongen_US Mai Universityen_US Universityen_US
Appears in Collections:CMUL: Journal Articles

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