The generation mechanism of synthetic minority class examples

The class imbalance problem, which exists in the field of medical image analysis universally, may cause a significant deterioration to the performance of the standard classifiers. In this paper, the related work on dealing with class imbalance is firstly reviewed, and then a proper generation mechanism of synthetic minority class examples is discussed. According to the analysis, a novel oversampling algorithm with synthetic examples, ADOMS, is proposed by generating synthetic examples along the first principal component axis of local data distribution. The experiments are arranged on 12 UCI datasets and the experimental results show that comparing with other relative methods, algorithm ADOMS is able to alleviate the deterioration of the classification performance effectively.