Matches in SemOpenAlex for { <https://semopenalex.org/work/W1995244252> ?p ?o ?g. }
- W1995244252 abstract "Effectively predicting corporate financial distress is an important and challenging issue for companies. The research aims at predicting financial distress using the integrated model of rough set theory (RST) and support vector machine (SVM), in order to find a better early warning method and enhance the prediction accuracy. After several comparative experiments with the dataset of Chinese listed companies, rough set theory is proved to be an effective approach for reducing redundant information. Our results indicate that the SVM performs better than the BPNN when they are used for corporate financial distress prediction." @default.
- W1995244252 created "2016-06-24" @default.
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- W1995244252 date "2011-02-01" @default.
- W1995244252 modified "2023-09-25" @default.
- W1995244252 title "PREDICTING FINANCIAL DISTRESS OF CHINESE LISTED COMPANIES USING ROUGH SET THEORY AND SUPPORT VECTOR MACHINE" @default.
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- W1995244252 doi "https://doi.org/10.1142/s0217595911003077" @default.
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