Open Access

ARTICLE

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

Xuewen Mu^*, Bingcong Zhao

School of Mathematics and Statistics, Xidian University, Xi’an, 710071, China

* Corresponding Author: Xuewen Mu. Email:

Computers, Materials & Continua 2025, 83(2), 2143-2159. https://doi.org/10.32604/cmc.2025.060739

Received 08 November 2024; Accepted 28 January 2025; Issue published 16 April 2025

Abstract

When dealing with imbalanced datasets, the traditional support vector machine (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 vector machine (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 with multiple majority class datasets can obtain multiple 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.

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Keywords

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