Random Forest and Novel Under-Sampling Strategy for Data Imbalance in Software Defect Prediction

Utomo Pujianto; . .

doi:10.14419/ijet.v7i4.15.21368

Authors

Utomo Pujianto
. .

Received date: October 9, 2018

Accepted date: October 9, 2018

Published date: October 7, 2018

DOI:

https://doi.org/10.14419/ijet.v7i4.15.21368

Keywords:

Data imbalance, Random forests, Software defect prediction, Under-sampling.

Abstract

Data imbalance is one among characteristics of software quality data sets that can have a negative effect on the performance of software defect prediction models. This study proposed an alternative to random under-sampling strategy by using only a subset of non-defective data which have been calculated as having biggest distance value to the centroid of defective data. Combined with random forest Â Â Â Â Â Â classification, the proposed method outperformed both the random under-sampling and non-sampling method on the basis of accuracy, AUC, f-measure, and true positive rate performance measures.
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