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Combining multiple data resampling methods and classifier ensembles for better financial distress prediction: homogeneous and heterogeneous approaches.

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Title: Combining multiple data resampling methods and classifier ensembles for better financial distress prediction: homogeneous and heterogeneous approaches.
Authors: Hu, Ya-Han1, Tsai, Chih-Fong1, cftsai@mgt.ncu.edu.tw, Wang, Pei-Ting1
Source: Annals of Operations Research; Oct2025, Vol. 353 Issue 2, p793-814, 22p
Database: Applied Science & Technology Source
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  Data: Combining multiple data resampling methods and classifier ensembles for better financial distress prediction: homogeneous and heterogeneous approaches.
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  Data: <searchLink fieldCode="JN" term="%22Annals+of+Operations+Research%22">Annals of Operations Research</searchLink>; Oct2025, Vol. 353 Issue 2, p793-814, 22p
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=aci&AN=188685379
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      – Type: doi
        Value: 10.1007/s10479-025-06706-5
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      – Code: eng
        Text: English
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        PageCount: 22
        StartPage: 793
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      – TitleFull: Combining multiple data resampling methods and classifier ensembles for better financial distress prediction: homogeneous and heterogeneous approaches.
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            NameFull: Hu, Ya-Han
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            NameFull: Tsai, Chih-Fong
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            NameFull: Wang, Pei-Ting
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              Text: Oct2025
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              Y: 2025
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