SMOTE, Borderline-SMOTE, SVM-SMOTE, ADASYN — when do you use each?
hardAnswer
- Vanilla SMOTE oversamples uniformly, including points deep in the class interior (redundant).
- Borderline-SMOTE: only synthesize from minority points near the decision boundary (their kNN mixes both classes) — where the model actually needs help.
- SVM-SMOTE: fit an SVM, synthesize near the support vectors.
- ADASYN: synthesize more around minority points that are hard to classify (many majority neighbours), less around easy ones — adaptive.
- In practice: try Borderline-SMOTE and ADASYN before vanilla — usually a small but consistent improvement.
Check yourself — multiple choice
- They're all identical
- Borderline / SVM-SMOTE focus near the decision boundary; ADASYN adapts to hard-to-classify points
- ADASYN is only for regression
- SVM-SMOTE doesn't use SVMs
Boundary-focused (Borderline, SVM-SMOTE) or difficulty-adaptive (ADASYN) usually beat vanilla SMOTE.
#imbalance
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