关键词: 不平衡数据集, 分类, 过采样, 特征边界

Abstract:

Aiming at the problem of unbalanced distribution of datasets in practical application, this paper proposes a oversampling technique which combines feature boundary data information. The proposed method removes the noise points in the dataset firstly, then based on the majority near-neighbors sets of minority class samples and the geometric distribution information, some minority samples which are helpful to define optimal nonlinear classification boundary are chosen to generate new samples in combination with clusters of minority sets. After using various oversampling techniques to deal with unbalanced datasets, the classification is carried out by using Support Vector Machine（SVM）, the comparison experiment shows that the proposed method effectively improves the classification accuracy of unbalanced data and verifies the validity of the algorithm.

Key words: imbalanced data set, classification, oversampling, characteristic boundary