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dc.contributor.authorYang, Z. J.zh_CN
dc.contributor.authorYou, W. J.zh_CN
dc.contributor.authorJi, G. L.zh_CN
dc.contributor.author吉国力zh_CN
dc.date.accessioned2013-12-12T02:49:26Z
dc.date.available2013-12-12T02:49:26Z
dc.date.issued2011-07zh_CN
dc.identifier.citationExpert Systems with Applications, 2011,38(7):8336-8342zh_CN
dc.identifier.issn0957-4174zh_CN
dc.identifier.otherISI:000289047700049zh_CN
dc.identifier.urihttps://dspace.xmu.edu.cn/handle/2288/70703
dc.descriptionNational Natural Science Foundation of China [60774033]; Specialized Research Fund for the Doctoral Program of Higher Education of China [20070384003, 20090121110022]; Key Research Project of Fujian Province of China [2009H0044]; Xiamen University of Chinzh_CN
dc.description.abstractThe evaluation of corporate financial distress has attracted significant global attention as a result of the increasing number of worldwide corporate failures. There is an immediate and compelling need for more effective financial distress prediction models. This paper presents a novel method to predict bankruptcy. The proposed method combines the partial least squares (PLS) based feature selection with support vector machine (SVM) for information fusion. PLS can successfully identify the complex nonlinearity and correlations among the financial indicators. The experimental results demonstrate its superior predictive ability. On the one hand, the proposed model can select the most relevant financial indicators to predict bankruptcy and at the same time identify the role of each variable in the prediction process. On the other hand, the proposed model's high levels of prediction accuracy can translate into benefits to financial organizations through such activities as credit approval, and loan portfolio and security management. (C) 2011 Elsevier Ltd. All rights reserved.zh_CN
dc.language.isoen_USzh_CN
dc.source.urihttp://dx.doi.org/10.1016/j.eswa.2011.01.021zh_CN
dc.subjectNEURAL-NETWORKzh_CN
dc.subjectGENETIC ALGORITHMSzh_CN
dc.subjectMODELzh_CN
dc.subjectSELECTIONzh_CN
dc.subjectENSEMBLEzh_CN
dc.subjectFAILUREzh_CN
dc.titleUsing partial least squares and support vector machines for bankruptcy predictionzh_CN
dc.typeArticlezh_CN


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