ADASYN operates by focusing on the minority class samples that are harder to learn. The algorithm calculates the density distribution for minority class samples and creates synthetic samples based on this density. Specifically, ADASYN performs the following steps:
For each minority class sample, calculate the number of similar samples in its neighborhood. Determine the difficulty level of learning each minority sample based on its neighborhood density. Generate synthetic samples by interpolating between minority samples and their neighbors, with more synthetic samples being generated for harder-to-learn samples.
