Over-Sampling Method on Imbalanced Data Based on WKMeans and SMOTE

被引：1

作者：

Chen, Junfeng ^{[1
]}

Zheng, Zhongtuan ^{[1
]}

机构：

[1] School of Mathematics, Physics and Statistics, Shanghai University of Engineering Science, Shanghai

来源：

Computer Engineering and Applications | 2024年 / 57卷 / 23期

关键词：

clustering consistency index; clustering ensembles; clusters matching; feature weighting; imbalanced data classification; over-sampling;

D O I：

10.3778/j.issn.1002-8331.2011-0215

中图分类号：

学科分类号：

摘要：

A new method for imbalanced data sets on feature weighting and clustering ensembles is proposed(WKMeans-SMOTE), which aims to solve the problem of synthesizing all the minority samples without any guidance in SMOTE method. Firstly, considering the different degree of impact of different feature weights on the clustering results, a new clustering algorithm with different feature weights is selected. The initial cluster center is changed many times to generate different clustering results.Then,clustering results are aligned based on the idea of matching clusters algorithm,and the cluster boundary minority samples are picked by introducing clustering consistency index. Finally, the SMOTE method is used on those picked minority samples, and CART algorithm is used as the base classifier to train the balanced dataset. The experimental results show that the method achieves better classifying quality on F-value and G-mean compared with SMOTE, Borderline-SMOTE, ADASYN and other oversampling methods. © 2024 Journal of Computer Engineering and Applications Beijing Co., Ltd.; Science Press. All rights reserved.

引用

页码：106 / 112

页数：6

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