Congcong Ma1,2, Jiaqi Mi1, Wanlin Gao1,2, Sha Tao1,2,*
CMC-Computers, Materials & Continua, Vol.80, No.3, pp. 4243-4261, 2024, DOI:10.32604/cmc.2024.054506
Abstract Imbalanced data classification is the task of classifying datasets where there is a significant disparity in the number of samples between different classes. This task is prevalent in practical scenarios such as industrial fault diagnosis, network intrusion detection, cancer detection, etc. In imbalanced classification tasks, the focus is typically on achieving high recognition accuracy for the minority class. However, due to the challenges presented by imbalanced multi-class datasets, such as the scarcity of samples in minority classes and complex inter-class relationships with overlapping boundaries, existing methods often do not perform well in multi-class imbalanced data… More >
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