Hybridizing principles of the Electre method with case-based reasoning for data mining: Electre-CBR-I and Electre-CBR-II

European Journal of Operational Research

Volumn 197, Issue 1, 2009, Pages 214-224

  Li, Hui ^a   Sun, Jie ^a  

^a ZHEJIANG NORMAL UNIVERSITY   (China)

Author keywords

30 times hold out method; Case based reasoning; Data mining; Electre; Financial distress prediction

Indexed keywords

CASE BASED REASONING; FINANCE; INFORMATION MANAGEMENT; INFORMATION SERVICES; LEARNING SYSTEMS;

30-TIMES HOLD-OUT METHOD; BASIC IDEAS; CROSS VALIDATIONS; DATA DISTRIBUTIONS; DATA MINING; ELECTRE; EMPIRICAL RESULTS; FINANCIAL DISTRESS; FINANCIAL DISTRESS PREDICTION; FINANCIAL RATIOS; FUNDAMENTAL PRINCIPLES; GRID SEARCHES; HYBRID MODELS; LEAVE ONE OUTS; MACHINE-LEARNING; OPTIMAL PARAMETERS; OUTRANKING METHODS; PREDICTIVE PERFORMANCE; SIMILARITY MEASURES;

BITS;

EID: 59649108839     PISSN: 03772217     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.ejor.2008.05.024     Document Type: Article

References (54)

1. Altman E. Financial ratios discriminant analysis and the prediction of corporate bankruptcy. Journal of Finance 23 (1968) 589-609
2. Back B., Laitinen T., and Sere K. Neural network and genetic algorithm for bankruptcy prediction. Expert Systems with Applications 11 (1996) 407-413
3. Beaver W. Financial ratios as predictors of failure. Journal of Accounting Research 4 (1966) 71-111
4. Bi J., Bennett K., Embrechts M., et al. Dimensionality reduction via sparse support vector machines. Journal of Machine Learning Research 3 (2003) 1229-1243
5. Bouyssou D., and Pirlot M. A characterization of concordance relations. European Journal of Operational Research 167 (2005) 427-443
6. Brans J.P., and Vincke Ph. A preference ranking organization method: The Promethee method for multiple criteria decision making. Management Science 31 (1985) 647-656
7. Brown C.E., and Gupta U. Applying case-based reasoning to the accounting domain. Intelligent Systems in Accounting, Finance and Management 3 (1994) 205-221
8. Bryant S.M. A case-based reasoning approach to bankruptcy prediction modeling. Intelligent Systems in Accounting, Finance and Management 6 (1997) 195-214
9. Chen L.-H., and Hsiao H.-D. Feature selection to diagnose a business crisis by using a real GA-based support vector machine: An empirical study. Expert Systems with Applications 35 3 (2008) 1145-1155
10. Ding Y.-S., Song X.-P., and Zen Y.-M. Forecasting financial condition of Chinese listed companies based on support vector machine. Expert Systems with Applications 34 (2008) 3081-3089
11. Guyon I., and Elisseeff A. An introduction to variable and feature selection. Journal of Machine Learning Research 3 (2003) 1157-1182
12. Han J.-W., and Kamber M. Data Mining Concepts and Techniques (2001), Morgan Kaufman, San Mateo
13. Hand D., Mannila H., and Smyth P. Principles of Data Mining (2001), MIT Press, Cambridge
14. Hua Z.-S., Wang Y., Xu X.-Y., et al. Predicting corporate financial distress based on integration of support vector machine and logistic regression. Expert Systems with Applications 33 (2007) 434-440
15. Hui X., and Sun J. An application of support vector machine to companies' financial distress prediction. In: Torra V., Narukawa Y., Valls A., et al. (Eds). Modeling Decisions for Artificial Intelligence (2006), Springer-Verlag, Berlin 274-282
16. Jo H., and Han I. Integration of case-based forecasting, neural network, and discriminant analysis for bankruptcy prediction. Expert Systems with Applications 11 (1996) 415-422
17. Jo H., Han I., and Lee H. Bankruptcy prediction using case-based reasoning, neural network and discriminant analysis for bankruptcy prediction. Expert Systems with Applications 13 (1997) 97-108
18. Kim K.-J. Toward global optimization of case-based reasoning systems for financial forecasting. Applied Intelligence 21 (2004) 239-249
19. Kolodner J.L. Case-Based Reasoning (1993), Morgan Kaufman, San Francisco
20. Kumar P.R., and Ravi V. Bankruptcy prediction in banks and firms via statistical and intelligent techniques - A review. European Journal of Operational Research 180 (2007) 1-28
21. Li H., and Sun J. Ranking-order case-based reasoning for financial distress prediction. Knowledge-Based Systems (2008) 10.1016/j.knosys.2008.03.047
22. Liang L., and Wu D. An application of pattern recognition on scoring Chinese corporations financial conditions based on back propagation neural network. Computers & Operations Research 32 (2005) 1115-1129
23. Li H., Sun J., and Sun B. Financial distress prediction based on OR-CBR in the principle of k-nearest neighbors. Expert Systems with Applications (2007) 10.1016/j.eswa.2007.09.038
24. Lin F.-Y., and McClean S. A data mining approach to the prediction of corporate failure. Knowledge-Based Systems 14 (2001) 189-195
25. Lin R.-H., Wang Y.-T., Wu C.-H., et al. Developing a business failure prediction model via RST, GRA and CBR. Expert Systems with Applications (2007) 10.1016/j.eswa.2007.11.068
26. Martin D. Early warning of bank failure: A Logit regression approach. Journal of Banking and Finance 1 (1977) 249-276
27. Min S.-H., Lee J.-M., and Han I. Hybrid genetic algorithms and support vector machines for bankruptcy prediction. Expert Systems with Applications 31 (2006) 652-660
28. Odom, M., Sharda, R., 1990. A neural networks model for bankruptcy prediction. In: Proceedings of International Joint Conference on Neural Networks, San Diego, CA, pp. 163-168.
29. Pal S.K., and Shiu S. Foundations of Soft Case-Based Reasoning (2004), Wiley, New Jersey
30. Pan R., Yang Q., and Pan J. Mining competent case bases for case-based reasoning. Artificial Intelligence 171 (2007) 1039-1068
31. Park C.-S., and Han I. A case-based reasoning with the feature weights derived by analytic hierarchy process for bankruptcy prediction. Expert Systems with Applications 23 (2002) 255-264
32. Roy B. Problems and methods with multiple objective functions. Mathematical Programming 1 (1971) 239-266
33. Roy B. The outranking approach and the foundations of ELECTRE methods. Theory and Decision 31 (1991) 49-73
34. Roy B. Multicriteria Methodology for Decision Aiding (1996), Kluwer Academic Publishers, Dordrecht
35. Roy B., and Słowiński R. Handling effects of reinforced preference and counter-veto in credibility of outranking. European Journal of Operational Research 188 (2008) 185-190
36. Roy B., and Vincke P. Relational systems of preference with one or more pseudo-criteria: Some new concepts and results. Management Science 30 (1984) 1323-1335
37. Schank R. Dynamic Memory (1982), Cambridge University Press, New York
38. Schank R., and Abelson R. Scripts, Plans, Goals and Understanding (1977), Erlbaum Hillsdale, New Jersey
39. Shin K.-S., Lee T.-S., and Kim H.-J. An application of support vector machines in bankruptcy prediction model. Expert Systems with Applications 28 (2005) 127-135
40. Shiu S.C.K., and Pal S.K. Case-based reasoning: Concepts, features and soft computing. Applied Intelligence 21 (2004) 233-238
41. Slowinski R., and Stefanowski J. Rough classification with valued closeness relation. In: Diday E., Lechevallier Y., Schrader M., et al. (Eds). New Approaches in Classification and Data Analysis (1994), Springer-Verlag, Berlin 482-489
42. Slowinski R., and Stefanowski J. Rough set reasoning about uncertain data. Fundamenta Informaticae 27 (1996) 229-243
43. Smet Y.D., and Guzman L.M. Towards multicriteria clustering: An extension of the k-means algorithm. European Journal of Operational Research 158 (2004) 390-398
44. Sun J., and Hui X.-F. Financial distress prediction based on similarity weighted voting CBR. In: Li X., Zaiane R., and Li Z. (Eds). Advanced Data Mining and Applications (2006), Springer-Verlag, Berlin 947-958
45. Sun J., and Li H. Financial distress early warning based on group decision making. Computers & Operations Research (2007) 10.1016/j.cor.2007.11.005
46. Sun J., and Li H. Data mining method for listed companies' financial distress prediction. Knowledge-Based Systems 21 (2008) 1-5
47. Sun J., and Li H. Listed companies' financial distress prediction based on weighted majority voting combination of multiple classifiers. Expert Systems with Applications 35 3 (2008) 818-827
48. Takeda E. A method for multiple pseudo-criteria decision problems. Computers & Operations Research 28 (2001) 1427-1439
49. Vincke Ph. Analysis of MCDA in Europe. European Journal of Operational Research 25 (1986) 160-168
50. Watson I. Case-based reasoning is methodology, not a technology. Knowledge-Based Systems 12 (1999) 303-308
51. Witten I.H., and Frank E. Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations (2000), Morgan Kaufman, San Francisco, CA
52. Yip A., and Deng H. A case-based reasoning approach to business failure prediction. In: Palade V., Howlett R.J., and Jain L.C. (Eds). Knowledge-Based Intelligent Information and Engineering Systems (2003), Springer-Verlag, Berlin 1075-1080
53. Yip A. Predicting business failure with a case-based reasoning approach. In: Negoita M., Howlett R., Jain L., et al. (Eds). Knowledge-Based Intelligent Information and Engineering Systems (2004), Springer-Verlag, Berlin 665-671
54. Zhou Z.-H. Three perspectives of data mining. Artificial Intelligence 143 (2003) 139-146