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

 
 
 
  • Abstract
  • Keywords
  • References
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  • 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.

     

     


  • Keywords


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

  • References


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Article ID: 21368
 
DOI: 10.14419/ijet.v7i4.15.21368




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