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A data mining approach to the prediction of corporate failure

Biomedical Sciences Research Institute Computer Science Research Institute Environmental Sciences Research Institute Nanotechnology & Advanced Materials Research Institute

Lin, FY and McClean, SI (2001) A data mining approach to the prediction of corporate failure. Knowledge-Based Systems, 14 (3-4). pp. 189-195. [Journal article]

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DOI: 10.1016/S0950-7051(01)00096-X

Abstract

This paper uses a data mining approach to the prediction of corporate failure. Initially, we use four single classifiers - discriminant analysis, logistic regression, neural networks and C5.0 - each based on two feature selection methods for predicting corporate failure. Of the two feature selection methods - human judgement based on financial theory and ANOVA statistical method - we found the ANOVA method performs better than the human judgement method in all classifiers except discriminant analysis. Among the individual classifiers, decision trees and neural networks were found to provide better results. Finally, a hybrid method that combines the best features of several classification models is developed to increase the prediction performance. The empirical tests show that such a hybrid method produces higher prediction accuracy than individual classifiers.

Item Type:Journal article
Faculties and Schools:Faculty of Computing & Engineering
Faculty of Computing & Engineering > School of Computing and Information Engineering
Research Institutes and Groups:Computer Science Research Institute
Computer Science Research Institute > Information and Communication Engineering
ID Code:48
Deposited By:Professor Sally McClean
Deposited On:23 Sep 2009 17:30
Last Modified:23 Sep 2009 17:30

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