ADASYN (improves class balance, extension of SMOTE)

ADASYN algorithm to reduce class imbalance by synthesizing minority class examples
Updated 23 Apr 2015

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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.

Cite As

Dominic Siedhoff (2024). ADASYN (improves class balance, extension of SMOTE) (, MATLAB Central File Exchange. Retrieved .

MATLAB Release Compatibility
Created with R2011b
Compatible with any release
Platform Compatibility
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Version Published Release Notes

[2015-04-23] improved quality of results in the presence of flat dimensions in the input data [2015-04-21]:
Boundary cases (0 or 1 examples in minority class, classes already balanced) now result in warnings instead of errors.