Handling imbalanced data sets with synthetic boundary data generation using bootstrap re-sampling and AdaBoost techniques
The location of separating function in each class is defined by the boundary data. The synthetic boundary data are generated by bootstrapping. All new synthesized data were classified by AdaBoost neural network. The proposed method achieves higher accuracy in both minority and majority classes.
imbalanced data boundary data bootstrap re-sampling adaboost synthetic data generation