ADASYN
While SMOTE doesn’t distinguish between the density distribution of minority class samples, Adaptive Synthetic Sampling (ADASYN) [6] focuses on harder-to-classify minority class samples since they are in a low-density area. ADASYN uses a weighted distribution of the minority class based on the difficulty of classifying the observations. This way, more synthetic data is generated from harder samples:
Figure 2.11 – Illustration of how ADASYN works
Here, we can see the following:
- a) The majority and minority class samples are plotted
- b) Synthetic samples are generated depending on the hardness factor (explained later)
While SMOTE uses all samples from the minority class for oversampling uniformly, in ADASYN, the observations that are harder to classify are used more often.
Another difference between the two techniques is that, unlike SMOTE, ADASYN also uses the majority class observations while training KNN. It then...
