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

  • Authors

    • Utomo Pujianto
    • . .
    https://doi.org/10.14419/ijet.v7i4.15.21368

    Received date: October 9, 2018

    Accepted date: October 9, 2018

    Published date: October 7, 2018

  • 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       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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  • How to Cite

    Pujianto, U., & ., . (2018). Random Forest and Novel Under-Sampling Strategy for Data Imbalance in Software Defect Prediction. International Journal of Engineering and Technology, 7(4.15), 39-42. https://doi.org/10.14419/ijet.v7i4.15.21368