MAHAKIL: Diversity Based Oversampling Approach to Alleviate the Class Imbalance Issue in Software Defect Prediction

• Main Authors: Kwabena Ebo Bennin, Jacky Keung, Passakorn Phannachitta, Akito Monden, Solomon Mensah
• 格式: 雜誌
• 出版: 2018
• 主題: Computer Science
• 在線閱讀: https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85028936214&origin=inward; http://cmuir.cmu.ac.th/jspui/handle/6653943832/58498
• 機構: Chiang Mai University

© 1976-2012 IEEE. Highly imbalanced data typically make accurate predictions difficult. Unfortunately, software defect datasets tend to have fewer defective modules than non-defective modules. Synthetic oversampling approaches address this concern by creating new minority defective modules to balanc...