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• W2236276366 abstract "The economic and social problems associated with business bankruptcy have spurred searches for better theoretical understanding and prediction capability. To enhance the predictive performance, this paper includes K-mean Clustering, Rough Sets and Support Vector Machines into the modelling process of business failure research. An empirical study on 66 failed firms and 66 one-to-one matching non-failed firms in Taiwan between 1998 and 2009 is conducted, using available data for the six years before bankruptcy. The developed integrated prediction model in this research is 89% accurate on a validation sample as compared to the original rough sets model which was 78% accurate. The empirical study shows that K-means clustering and support vector machines play an essential role in enhancing the performance of rough set models. These findings highlight the effectiveness of integrated prediction models for business bankruptcy prediction and particularly the importance of K-means clustering and support vector machines coupled with rough sets in business failure research." @default.
• W2236276366 created "2016-06-24" @default.
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• W2236276366 date "2013-12-01" @default.
• W2236276366 modified "2023-09-25" @default.
• W2236276366 title "An Integrated Business Bankruptcy Prediction Model based on K-mean Clustering, Rough Sets and Support Vector Machines" @default.
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