Abstract
This paper presents case-based reasoning approach for financial crises warning. Unlike problem solving processes of the traditional rule-based reasoning approach, in which users directly obtain the problem’s solution by rule series, CBR is a methodology providing adaptive solu-tions by systematic comparison between current situation and the simi-lar cases stored in the case library. Three critical techniques of CBR are studied intensively in this paper, namely: knowledge representation, re-trieval of similar cases and case learning. At the end of the paper, an implementation of the Case-based Financial Crises Warning Prototype System (CFCWPS) is described in summary.
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References
Berg, A., Pattillo, C.: Predicting currency crises: The indicators approach and an alternative. Journal of International Money and Finance 18, 561–586 (1999)
Zhiqiang, L.: Evaluation to the methods of forecasting currency crises. World Economy 7, 16–21 (2000)
Kaminsky, G.L., Lizondo, S., Reinhart, C.M.: Leading Indicators of Currency Crises. IMF WorkPaper 97/79 (1997)
David, W.: Aha:The omnipresence of case-based reasoning in science and application. Knowledge-based systems 11, 261–278 (1998)
Becerra-Fernandez, I., Aha, D.W.: Case-Based Problem Solving for Knowledge Management System. In: Proceedings of the 12th Annual International FLAIRS, NCARAI Technical Report AIC-99-005
Watson, I.: Case-based reasoning is a methodology not a technology. Knowledge- Based Systems 12, 303–308 (1999)
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Wei, Y., Li, F. (2003). Case-Based Reasoning: An Intelligent Approach Applied for Financial Crises Warning. In: Liu, J., Cheung, Ym., Yin, H. (eds) Intelligent Data Engineering and Automated Learning. IDEAL 2003. Lecture Notes in Computer Science, vol 2690. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-45080-1_134
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DOI: https://doi.org/10.1007/978-3-540-45080-1_134
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-40550-4
Online ISBN: 978-3-540-45080-1
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