This paper aims to develop an integrated model of predicting business failure, using business financial and non-financial factors to diagnose the status of business, thereby providing useful references for business operation. This study applied Rough Set Theory to extract key financial and non-financial factors and Grey Relational Analysis (GRA) as the approach of assigning weights. In addition, Case-Based Reasoning (CBR) are adopted to propose a new hybrid models entitled RG-CBR (combining RST and CBR with GRA) to compare the accuracy rates in predicting failure. After exploring the TEJ (Taiwan Economic Journal) database and conducting various experiments with CBR, RST-CBR and RG-CBR the study finds CBR, RST-CBR and RG-CBR reporting an accuracy rate in predicting business failure of 49.2%, 59.8% and 83.3%respectively. The RG-CBR boasts the highest accuracy rate while also effectively reducing Type I and Type II error rates. © 2010 Springer-Verlag Berlin Heidelberg.
CITATION STYLE
Lin, R. H., & Chuang, C. L. (2010). Development of a hybrid case-based reasoning for bankruptcy prediction. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 6018 LNCS, pp. 178–188). Springer Verlag. https://doi.org/10.1007/978-3-642-12179-1_17
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