关键词: 支持k-离群度, 重采样, 边界点, 非平衡数据分类

Abstract: In the problem of imbalanced data classification, aiming to synthesize valuable new samples and delete the original samples without any influence, a novel imbalanced data classification method based on boundary mixed resampling is proposed. Firstly, the concept of k-outlier is introduced to find out the boundary and non-boundary samples and then deal with them in different ways. The minority samples in boundary are taken as the target points to synthesize new sample points while the non-boundary majority ones are under sampled based on distance to achieve a basic balance of samples. By comparing the experimental results, it shows that the proposed algorithm achieves a better classification performance on the classification accuracy of minority samples to some extent on the premise of ensuring a better G-mean value.

Key words: k-outlier, resampling, boundary points, imbalanced data classification