Random undersampling vs Tomek links vs cluster-centroid undersampling — what's each for?
mediumAnswer
- Random undersampling: drop majority-class rows uniformly to balance.
- Fast; wastes majority-class information.
- Tomek links: identify pairs of opposite-class points that are each other's nearest neighbours (boundary noise) and drop the majority-class member — cleans the decision boundary.
- Cluster centroids: cluster majority points, replace them with cluster centroids — preserves diversity while reducing count.
- Combine SMOTE + Tomek or SMOTE + ENN (edited nearest neighbours) to oversample the minority and clean the boundary in one pass.
Check yourself — multiple choice
- They all remove the same rows
- Random: uniform drop; Tomek: drop majority boundary noise; Cluster centroids: replace with cluster centers
- Tomek links add rows
- Undersampling requires the minority class
Random / Tomek (boundary noise) / cluster centroids — different tradeoffs of speed and information retention.
#imbalance
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