A data mining approach to the prediction of corporate failure

FY Lin, SI McClean

Research output: Contribution to journalArticle

95 Citations (Scopus)

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.
Original languageEnglish
Pages (from-to)189-195
JournalKnowledge-Based Systems
Volume14
Issue number3-4
DOIs
Publication statusPublished - 23 Jun 2001

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