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Adaptive sample repulsion against class-specific counterfactuals for explainable imbalanced classification.

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Title: Adaptive sample repulsion against class-specific counterfactuals for explainable imbalanced classification.
Authors: Hao Y; School of Intelligent Engineering and Automation, Beijing University of Posts and Telecommunications, Beijing, 100876, China. Electronic address: haoyu_bupt@bupt.edu.cn., Gao X; School of Intelligent Engineering and Automation, Beijing University of Posts and Telecommunications, Beijing, 100876, China. Electronic address: xlhhh74@bupt.edu.cn., Diao X; Metrology Research Institute, China Electric Power Research Institute Company Limited, Beijing, 100192, China. Electronic address: diaoxp@epri.sgcc.com.cn., Li Y; Metrology Research Institute, China Electric Power Research Institute Company Limited, Beijing, 100192, China. Electronic address: liyuan3@epri.sgcc.com.cn., Lin Y; Metrology Research Institute, China Electric Power Research Institute Company Limited, Beijing, 100192, China. Electronic address: linyukun@epri.sgcc.com.cn., Chen T; Metrology Research Institute, China Electric Power Research Institute Company Limited, Beijing, 100192, China. Electronic address: chentianyang@epri.sgcc.com.cn., Li Q; School of Intelligent Engineering and Automation, Beijing University of Posts and Telecommunications, Beijing, 100876, China. Electronic address: 2782709791@bupt.edu.cn., Lu J; School of Intelligent Engineering and Automation, Beijing University of Posts and Telecommunications, Beijing, 100876, China. Electronic address: lujiawen@bupt.edu.cn.
Source: Neural networks : the official journal of the International Neural Network Society [Neural Netw] 2026 Jul; Vol. 199, pp. 108652. Date of Electronic Publication: 2026 Jan 30.
Publication Type: Journal Article
Journal Info: Publisher: Pergamon Press Country of Publication: United States NLM ID: 8805018 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1879-2782 (Electronic) Linking ISSN: 08936080 NLM ISO Abbreviation: Neural Netw Subsets: MEDLINE
Database: MEDLINE Ultimate
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  Data: Adaptive sample repulsion against class-specific counterfactuals for explainable imbalanced classification.
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  Data: <searchLink fieldCode="AU" term="%22Hao+Y%22">Hao Y</searchLink>; School of Intelligent Engineering and Automation, Beijing University of Posts and Telecommunications, Beijing, 100876, China. Electronic address: haoyu_bupt@bupt.edu.cn.<br /><searchLink fieldCode="AU" term="%22Gao+X%22">Gao X</searchLink>; School of Intelligent Engineering and Automation, Beijing University of Posts and Telecommunications, Beijing, 100876, China. Electronic address: xlhhh74@bupt.edu.cn.<br /><searchLink fieldCode="AU" term="%22Diao+X%22">Diao X</searchLink>; Metrology Research Institute, China Electric Power Research Institute Company Limited, Beijing, 100192, China. Electronic address: diaoxp@epri.sgcc.com.cn.<br /><searchLink fieldCode="AU" term="%22Li+Y%22">Li Y</searchLink>; Metrology Research Institute, China Electric Power Research Institute Company Limited, Beijing, 100192, China. Electronic address: liyuan3@epri.sgcc.com.cn.<br /><searchLink fieldCode="AU" term="%22Lin+Y%22">Lin Y</searchLink>; Metrology Research Institute, China Electric Power Research Institute Company Limited, Beijing, 100192, China. Electronic address: linyukun@epri.sgcc.com.cn.<br /><searchLink fieldCode="AU" term="%22Chen+T%22">Chen T</searchLink>; Metrology Research Institute, China Electric Power Research Institute Company Limited, Beijing, 100192, China. Electronic address: chentianyang@epri.sgcc.com.cn.<br /><searchLink fieldCode="AU" term="%22Li+Q%22">Li Q</searchLink>; School of Intelligent Engineering and Automation, Beijing University of Posts and Telecommunications, Beijing, 100876, China. Electronic address: 2782709791@bupt.edu.cn.<br /><searchLink fieldCode="AU" term="%22Lu+J%22">Lu J</searchLink>; School of Intelligent Engineering and Automation, Beijing University of Posts and Telecommunications, Beijing, 100876, China. Electronic address: lujiawen@bupt.edu.cn.
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        Value: 10.1016/j.neunet.2026.108652
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        Text: English
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              Text: 2026 Jul
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