A modified adaptive synthetic sampling method for learning imbalanced datasets
In imbalanced learning, most supervised learning algorithms often fail to account for data distribution and present learning that is biassed towards the majority leading to unfavorable classification performance, particularly for the minority class samples. To tackle this problem, the ADASYN algorithm adaptively allocates weights to the minority class examples. A significant weight improves the possibility for the minority class sample serving as a seed sample in the synthetic sample generation process. However, it does not consider the noisy examples. Thus, this paper presents a modified method of ADASYN (M-ADASYN) for learning imbalanced datasets with noisy samples. M-ADASYN considers the distribution of minority class and creates noise-free minority examples by eliminating the noisy samples based on proximity to the original minority and majority class samples. The experimental outcomes confirm that the predictive performance of M-ADASYN is better than KernelADASYN, ADASYN, and SMOTE algorithm.