A universal framework for multi-class imbalanced learning based on Huffman Tree

doi:10.1016/j.asoc.2026.114631

A universal framework for multi-class imbalanced learning based on Huffman Tree

2026-01-10

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DOI：10.1016/j.asoc.2026.114631

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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.

A universal framework for multi-class imbalanced learning based on Huffman Tree

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