DCS-SOCP-SVM:A Novel Integrated Sampling and Classification Algorithm for Imbalanced Datasets  

作　　者：Xuewen Mu Bingcong Zhao 

机构地区：[1]School of Mathematics and Statistics,Xidian University,Xi’an,710071,China

出　　处：《Computers, Materials & Continua》2025年第5期2143-2159,共17页计算机、材料和连续体(英文)

基　　金：supported by the Natural Science Basic Research Program of Shaanxi(Program No.2024JC-YBMS-026).

摘　　要：When dealing with imbalanced datasets,the traditional support vectormachine(SVM)tends to produce a classification hyperplane that is biased towards the majority class,which exhibits poor robustness.This paper proposes a high-performance classification algorithm specifically designed for imbalanced datasets.The proposed method first uses a biased second-order cone programming support vectormachine(B-SOCP-SVM)to identify the support vectors(SVs)and non-support vectors(NSVs)in the imbalanced data.Then,it applies the synthetic minority over-sampling technique(SV-SMOTE)to oversample the support vectors of the minority class and uses the random under-sampling technique(NSV-RUS)multiple times to undersample the non-support vectors of the majority class.Combining the above-obtained minority class data set withmultiple majority class datasets can obtainmultiple new balanced data sets.Finally,SOCP-SVM is used to classify each data set,and the final result is obtained through the integrated algorithm.Experimental results demonstrate that the proposed method performs excellently on imbalanced datasets.

关 键 词：DCS-SOCP-SVM imbalanced datasets sampling method ensemble method integrated algorithm 

分 类 号：TP181[自动化与计算机技术—控制理论与控制工程]