MAHAKIL : Diversity based Oversampling Approach to Alleviate the Class Imbalance Issue in Software Defect Prediction
Related Research Unit(s)
|Journal / Publication||IEEE Transactions on Software Engineering|
|Early online date||24 Jul 2017|
|State||Published - Jun 2018|
|Link to Scopus||https://www.scopus.com/record/display.uri?eid=2-s2.0-85028936214&origin=recordpage|
approaches, based on Brunner’s statistical significance test and Cliff’s effect sizes. Therefore, MAHAKIL is strongly recommended as an efficient alternative for defect prediction models built on highly imbalanced datasets.
- Class imbalance learning, Classification problems, Data sampling methods, Software defect prediction, Synthetic sample generation
MAHAKIL : Diversity based Oversampling Approach to Alleviate the Class Imbalance Issue in Software Defect Prediction. / Bennin, Kwabena Ebo; Keung, Jacky; Phannachitta, Passakorn; Monden, Akito; Mensah, Solomon.In: IEEE Transactions on Software Engineering, Vol. 44, No. 6, 06.2018, p. 534-550.