Name
Title | ||
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Predicting business failure using multiple case-based reasoning combined with support vector machine |
Abstract | ||
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Financial distress prediction of business institutions is a long cherished topic concentrating on reducing loss of the society. Case-based reasoning (CBR) is an easily understandable methodology for problem solving. Support vector machine (SVM) is a new technology developed recently with high classification performance. Combining-classifiers system is capable of taking advantages of various single techniques to produce high performance. In this research, we develop a new combining-classifiers system for financial distress prediction, where four independent CBR systems with k-nearest neighbor (KNN) algorithms are employed as classifiers to be combined, and SVM is utilized as the algorithm fulfilling combining-classifiers. The new combining-classifiers system is named as Multiple CBR systems by SVM (Multi-CBR-SVM). The four CBR systems, respectively, are found on similarity measure on the basis of Euclidean distance metric, Manhattan distance metric, Grey coefficient metric, and Outranking relation metric. Outputs of independent CBRs are transferred as inputs of SVM to carry out combination. How to implement the combining-classifiers system with collected data is illustrated in detail. In the experiment, 83 pairs of sample companies in health and distress from Shanghai and Shenzhen Stock Exchange were collected, the technique of grid-search was utilized to get optimal parameters, leave-one-out cross-validation (LOO-CV) was used as assessment in parameter optimization, and predictive performances on 30-times hold-out data were used to make comparisons among Multi-CBR-SVM, its components and statistical models. Empirical results have indicated that Multi-CBR-SVM is feasible and validated for listed companies' business failure prediction in China. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1016/j.eswa.2009.01.013 | Expert Syst. Appl. |
Keywords | Field | DocType |
multiple case-based reasoning,support vector machine,cbr system,business failure prediction,financial distress prediction,independent cbr system,predicting business failure,new combining-classifiers system,combining-classifiers system,outranking relation metric,euclidean distance metric,combining-classifiers,chinese listed companies,manhattan distance metric,multiple cbr system,case base reasoning,distance metric,euclidean distance,statistical model,leave one out cross validation,stock exchange,k nearest neighbor | Data mining,Similarity measure,Computer science,Support vector machine,Euclidean distance,Business failure prediction,Stock exchange,Statistical model,Artificial intelligence,Case-based reasoning,Business failure,Machine learning | Journal |
Volume | Issue | ISSN |
36 | 6 | Expert Systems With Applications |
Citations | PageRank | References |
31 | 0.90 | 32 |
Authors | ||
2 | ||