Open access
2023
COMPARATIVE EVALUATION OF CLASS-IMBALANCE CORRECTION TECHNIQUES FOR SOFTWARE DEFECT PREDICTION
This study compared no correction, random oversampling, random undersampling, SMOTE, ADASYN, and class-weighted learning across logistic regression, decision tree, random forest, support vector machine, and neural network classifiers to find accuracy alone is unsuitable for selecting defect predictors.
L. Akpan
· International Journal of App... · 0 citations