An Under-Sampling Algorithm Based on SVM
Zheng Hengyu · 2021
Tradition classification algorithms often get poor performance in imbalanced datasets because they are proposed under the assumption that the datasets are nearly balanced. Random under-sampling(RUS) algorithm is a popular algorithm to solve imbalance problem through removing some majority class samples randomly. However, RUS algorithm may neglect some key information of datasets. A new under-sampling algorithm based on SVM is proposed in this paper. The proposed algorithm aims to reserve samples distribution information in undersampling process. The simulation results show that the proposed algorithm could achieve satisfying performance.