We propose the imECOC method which works on dichotomies to handle both the between-class imbalance and within-class imbalance.
Experimental results on fourteen data sets show that, imECOC performs significantly better than many state-of-the-art multi-class imbalance learning methods, no ...
To enable ECOC to tackle multi-class imbalance, it is desired to have an appropriate code matrix, an effective learning strategy and a decoding strategy ...
Jun 24, 2024 · To enable ECOC to tackle multi-class imbalance, it is desired to have an appropriate code matrix, an effective learning strategy and a decoding ...
[PDF] Learning Imbalanced Multi-class Data with Optimal Dichotomy ...
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The imECOC method is proposed, which works on dichotomies to handle both the between-class imbalance and within- class imbalance, and performs significantly ...
Oct 5, 2023 · You can try oversampling the minority classes or undersampling the majority classes, or combine both together depending on the context.
Missing: Dichotomy | Show results with:Dichotomy
Bibliographic details on Learning Imbalanced Multi-class Data with Optimal Dichotomy Weights.
Nov 9, 2022 · Use class weights in your classifier, that should suffice. It basically penalises the incorrectly classified examples from the minority ...
Missing: Optimal Dichotomy
Learning Imbalanced Multi-class Data with Optimal Dichotomy Weights. X. Liu, Q. Li, and Z. Zhou. ICDM, page 478-487. IEEE Computer Society, (2013 ). 1. 1 ...
Learning imbalanced multi-class data with optimal dichotomy weights. IEEE 13th International Conference on Data Mining (IEEE ICDM), 2013 (PP. 478-487) ...