@Article{cmc.2025.060739,
AUTHOR = {Xuewen Mu, Bingcong Zhao},
TITLE = {DCS-SOCP-SVM: A Novel Integrated Sampling and Classification Algorithm for Imbalanced Datasets},
JOURNAL = {Computers, Materials \& Continua},
VOLUME = {83},
YEAR = {2025},
NUMBER = {2},
PAGES = {2143--2159},
URL = {http://www.techscience.com/cmc/v83n2/60536},
ISSN = {1546-2226},
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.},
DOI = {10.32604/cmc.2025.060739}
}