Name
Title | ||
|---|---|---|
Gaussian case-based reasoning for business failure prediction with empirical data in China |
Abstract | ||
|---|---|---|
Case-based reasoning (CBR) is an easily understandable concept. Business failure prediction (BFP) is a valuable tool that can assist businesses take appropriate action when faced with the knowledge of the possibility of business failure. This study aims to improve the performance of a CBR system for BFP in terms of accuracy and reliability by constructing a new similarity measure, an area seldom researched in the domain of BFP. In order to fulfill this objective, we present a hybrid Gaussian CBR (GCBR) system and use it in BFP with empirical data in China. The heart of GCBR is similarity measure using Gaussian indicators. Feature distances between a pair of cases on each feature are transferred to Gaussian indicators by Gaussian transformations. A combiner is used to generate case similarity on the basis of the Gaussian indicators. Consensus of nearest neighbors is used to generate forecasting on the basis of case similarity. The new hybrid CBR system was empirically tested with data collected from the Shanghai Stock Exchange and Shenzhen Stock Exchange in China. We statistically validated our results by comparing them with multiple discriminant analysis, logistic regression, and two classical CBR systems. The results indicated that GCBR produces superior performance in short-term BFP of Chinese listed companies in terms of both predictive accuracy and coefficient of variation. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1016/j.ins.2008.09.003 | Inf. Sci. |
Keywords | Field | DocType |
case similarity,short-term bfp,gaussian transformation,new hybrid cbr system,cbr system,business failure prediction,gaussian case-based reasoning,hybrid gaussian cbr,classical cbr system,similarity measure,new similarity measure,gaussian indicator,empirical data,case base reasoning,data collection,empirical study,nearest neighbor,stock exchange,multiple discriminant analysis,logistic regression,coefficient of variation | Data mining,Similarity measure,Multiple discriminant analysis,Stock exchange,Gaussian,Artificial intelligence,Business failure,Case-based reasoning,Logistic regression,Mathematics,Machine learning,Empirical research | Journal |
Volume | Issue | ISSN |
179 | 1-2 | 0020-0255 |
Citations | PageRank | References |
44 | 1.13 | 44 |
Authors | ||
2 | ||