Regarding

• machine-learning

• inf-0615

• Common issue on One-vs-All approach

• Balcanced classes do not mean having the same number of examples in all classes

  • For example, a problem with a number of 9:1 for one of the classes. We probably want to do something to balance the classes, but we probably do not want the classes to have the same number (1:1), because in the real world the problem is probably something similar to 9:1. We can try to balance by 2:1.

Fixing by sampling

• https://tungmphung.com/imbalanced-learning-sampling-techniques/
• Undersampling
• Oversampling

SMOTE

• Uses the notion of distance (cannot be applied to feature vectors with factor features)
• https://rikunert.com/SMOTE_explained

Other techniques

• Ensemble methods (ensemble-learning)
• Data Augmentation