ADASYN (improves class balance, extension of SMOTE)

ADASYN algorithm to reduce class imbalance by synthesizing minority class examples

This submission implements the ADASYN (Adaptive Synthetic Sampling) algorithm as proposed in the following paper:
H. He, Y. Bai, E.A. Garcia, and S. Li, "ADASYN: Adaptive Synthetic Sampling Approach for Imbalanced Learning", Proc. Int'l. J. Conf. Neural Networks, pp. 1322-1328, (2008).
The purpose of the ADASYN algorithm is to improve class balance by synthetically creating new examples from the minority class via linear interpolation between existing minority class examples. This approach by itself is known as the SMOTE method (Synthetic Minority Oversampling TEchnique). ADASYN is an extension of SMOTE, creating more examples in the vicinity of the boundary between the two classes than in the interior of the minority class.
A demo script producing the title figure of this submission is provided.