adasyn

**ADASYN** (ADAptive SYNthetic sampling) is an **improvement over SMOTE that adaptively generates more synthetic samples in regions where minority examples are harder to learn** — focusing synthetic data generation on the minority samples near the decision boundary or surrounded by majority samples. **How ADASYN Works** - **Density Estimation**: For each minority sample, compute the ratio of majority neighbors within $k$ nearest neighbors. - **Difficulty**: Samples with more majority neighbors are "harder" — generate MORE synthetic samples near them. - **Adaptive**: The number of synthetic samples per minority example is proportional to its local difficulty. - **Smoothing**: Normalize the difficulty ratios to obtain sampling weights. **Why It Matters** - **Targeted**: Unlike SMOTE (which treats all minority samples equally), ADASYN focuses on the hardest regions. - **Decision Boundary**: More synthetic samples near the decision boundary = better learned boundary. - **Adaptive**: Automatically identifies which minority regions need the most augmentation. **ADASYN** is **smart SMOTE** — adaptively generating more synthetic samples where the minority class is hardest to learn.