TY  - EJOU
AU  - Mu, Xuewen 
AU  - Zhao, Bingcong 

TI  - DCS-SOCP-SVM: A Novel Integrated Sampling and Classification Algorithm for Imbalanced Datasets
T2  - Computers, Materials \& Continua

PY  - 2025
VL  - 83
IS  - 2
SN  - 1546-2226

AB  - 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.
KW  - DCS-SOCP-SVM; imbalanced datasets; sampling method; ensemble method; integrated algorithm

DO  - 10.32604/cmc.2025.060739