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Tackling Imbalanced Class on Cross-Project Defect Prediction Using Ensemble SMOTE
Aries Saifudin (a*,b), Spits Warnars Harco Leslie Hendric (b), Benfano Soewito (b), Ford Lumban Gaol (b), Edi Abdurachman (b), Yaya Heryadi (b)

a) Informatics Engineering, Pamulang University
Jl. Puspitek Raya no. 46, buaran, Serpong, Tangerang Selatan, banten, Indonesia
*aries.saifudin[at]gmail.com
b) Computer Science Department, Graduate Program-Doctor of Computer Science, Bina Nusantara university
Jakarta, Indonesia


Abstract

The dataset with imbalanced class can reduce the performance of the classifiers. In this study proposed a cross-project software defect prediction model that applies the SMOTE (Synthetic Minority Oversampling Technique) to balance classes in datasets and ensembles technique to reduce misclassification. The ensemble technique using AdaBoost and Bagging algorithms. The results of the study show that the model that integrates SMOTE and Bagging provides better performance. The proposed model can find more software defects and more precise.

Keywords: Cross-Project, Defect Prediction, SMOTE, Ensemble

Topic: Informatic and Information System

Link: https://ifory.id/abstract/eJDmEzPYqpRk

Conference: 2nd International Conference on Informatics, Engineering, Science and Technology (INCITEST 2019)

Plain Format | Corresponding Author (Aries Saifudin)

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