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Tackling Imbalanced Class on Cross-Project Defect Prediction Using Ensemble SMOTE
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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
A Saifudin - Personal Name
S W H L Hendric - Personal Name
B Soewito - Personal Name
F L Gaol - Personal Name
Edi Abdurachman - Personal Name
Y Heryadi - Personal Name
S W H L Hendric - Personal Name
B Soewito - Personal Name
F L Gaol - Personal Name
Edi Abdurachman - Personal Name
Y Heryadi - Personal Name
NONE
IOP Conf. Series: Materials Science and Engineering 662 (2019) 062011
electronic file
English
IOP Publishing
2019
England and Wales
Jil.662, Terbitan 6, Hlm. 062011
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