Classification Beyond Balance

My notes as I attempt to make sense of the literature on handling severely imbalanced classes for prediction in practice.

Table of Contents

1. Foundations
   1. Prelude to Foundation: Who needs balance?
   2. Foundation: Establishing prediction goals
   3. Second Foundation: Multi-layered approaches
   4. Foundation's Edge: The importance of the 1's
   5. Forward the Foundation: The importance of the 0's
2. Resampling Methods: do they work?
   1. Undersampling
   2. Oversampling
   3. Hybrid
3. Dimensionality Reduction: how to best capture the 1's?
4. Algorithms
5. Cost Sensitive Learning
6. Evaluation Metrics
7. References