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A universal framework for multi-class imbalanced learning based on Huffman Tree
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DOI：10.1016/j.asoc.2026.114631.png)
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摘要
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• A novel framework based on Huffman trees is proposed to decompose multi-class imbalance problems. • A Huffman Tree-Biased Support Vector Machine (HT-BSVM) is proposed. • The Arithmetic Optimization Algorithm (AOA) is employed to optimize hyperparameters for HT-BSVM. • Extensive experiments and non-parametric statistical tests show the effectiveness and robustness of the proposed strategy.
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IF:
6.6
论文数:
1.4W
被引数:
4.8W
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