Open AccessEnsemble Undersampling to Handle Unbalanced Class on Cross-Project Defect Prediction
Author(s) -
Aries Saifudin,
Yaya Heryadi,
Lukas Lukas
Publication year - 2019
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/662/6/062012
Subject(s) - undersampling , adaboost , computer science , software bug , software , random forest , machine learning , ensemble learning , class (philosophy) , artificial intelligence , data mining , pattern recognition (psychology) , support vector machine , programming language
There has been much research which proposed for cross-project software defect prediction models but no models that perform very well with various datasets in general. Software defect dataset usually imbalanced because it contains far more the not defected modules than the defected modules. Class imbalances in the dataset can reduce the performance of classifiers in the software defect prediction model. In this study proposed a Random Undersampling algorithm to balance classes and ensemble techniques to reduce misclassification. The ensemble technique used is the AdaBoost and Bagging algorithm. The results showed that the software defect prediction model that integrates the Random Undersampling algorithm and AdaBoost provides better performance and can find more defects than other models.