Genetic algorithm in designing fuzzy information retrieval-based classifier by principal component analysis

Computers and Industrial Engineering

Volumn 51, Issue 1, 2006, Pages 117-127

  Hu, Yi Chung ^a  

^a CHUNG YUAN CHRISTIAN UNIVERSITY   (Taiwan)

Author keywords

Bankruptcy prediction; Fuzzy sets; Genetic algorithm; Principal component analysis

Indexed keywords

CLASSIFICATION (OF INFORMATION); COMPUTER SIMULATION; FUZZY CONTROL; FUZZY SETS; INFORMATION RETRIEVAL SYSTEMS; MEMBERSHIP FUNCTIONS; PRINCIPAL COMPONENT ANALYSIS;

BANKRUPTCY PREDICTION; CLASSIFICATION PROBLEMS; INFORMATION RETRIEVAL-BASED CLASSIFIERS;

GENETIC ALGORITHMS;

EID: 33749066719     PISSN: 03608352     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.cie.2006.07.003     Document Type: Article

References (32)

1. Abonyi J., Roubos J.A., and Szeifert F. Data-driven generation of compact, accurate, and linguistically-sound fuzzy classifiers based on a decision-tree initialization. International Journal of Approximate Reasoning 32 1 (2003) 1-21
2. Agrawal R., Mannila H., Srikant R., Toivonen H., and Verkamo A.I. Fast discovery of association rules. In: Fayyad U.M., Piatetsky-Shapiro G., Smyth P., and Uthurusamy R. (Eds). Advances in knowledge discovery and data mining (1996), AAAI Press, Menlo Park 307-328
3. Altman E.L. Financial ratios, discriminant analysis and the prediction of corporate bankruptcy. Journal of Finance 23 (1968) 596
4. Andrzej O. Evolutionary algorithms for single and multicriteria design optimization (2002), Physica-Verlag, NY
5. Auer, P., Burgsteiner, H., & Maass, W. (2002). Reducing communication for distributed learning in neural networks. In Lecture Notes in Computer Science (vol. 2415, pp. 123-128). Heidelberg: Springer-Verlag.
6. Carse B., Fogarty T.C., and Munro A. Evolving fuzzy rule based controllers using genetic algorithms. Fuzzy Sets and Systems 80 (1996) 273-293
7. Chen S.M. Techniques and applications of fuzzy theory in document retrieval systems. In: Leondes C.T. (Ed). Fuzzy theory systems: techniques and applications Vol. 2 (1999), Academic Press, San Diego 691-715
8. Chen S.M., and Wang J.Y. Document retrieval using knowledge-based fuzzy information retrieval techniques. IEEE Transactions on Systems, Man, and Cybernetics 25 (1995) 793-803
9. Elomaa T., and Rousu J. General and efficient multisplitting of numerical attributes. Machine Learning 36 3 (1999) 201-244
10. Goldberg D.E. Genetic algorithms in search, optimization, and machine learning (1989), Addison-Wesley, MA
11. Grabisch, M., & Dispot, F. (1992). A comparison of some methods of fuzzy classification on real data. In: Proceedings of the 2nd International Conference on Fuzzy Logic and Neural Networks (pp. 659-662). Iizuka, Japan.
12. Guan S.U., and Zhu F. Class decomposition for GA-based classifier agents-a Pitt approach. IEEE Transactions on Systems, Man, and Cybernetics 34 1 (2004) 381-392
13. Holland J.H. Adaptation in natural and artificial systems (1975), University of Michigan Press, MA
14. Hu Y.C., and Tzeng G.H. Elicitation of classification rules by fuzzy data mining. Engineering Applications of Artificial Intelligence 16 7-8 (2003) 709-716
15. Ishibuchi H., Nakashima T., and Murata T. Performance evaluation of fuzzy classifier systems for multidimensional pattern classification problems. IEEE Transactions on Systems, Man, and Cybernetics 29 5 (1999) 601-618
16. Ishibuchi, H., Yamamoto, T., & Nakashima, T. (2001). Fuzzy data mining: effect of fuzzy discretization. In: Proceedings of the 1st IEEE international conference on data mining (pp. 241-248). San Jose, USA.
17. Ishibhchi H., Yamamoto T., and Nakashima T. Hybridization of fuzzy GBML approaches for pattern classification problems. IEEE Transactions on Systems, Man, and Cybernetics 35 2 (2005) 359-365
18. Jang J.S.R., and Sun C.T. Neuro-fuzzy modeling and control. Proceedings of the IEEE 83 3 (1995) 378-406
19. Jang J.S.R., Sun C.T., and Mizutani E. Neuro-fuzzy and soft computing: a computational approach to learning and machine Intelligence (1997), Prentice-Hall, NJ
20. Koutsougeras, C., & Papachristou, C. A. (1988). Learning discrete mappings: Athena's approach. In Proceedings of The IEEE International Conference on Languages for Automation.
21. Kuncheva L.I. Fuzzy classifier design (2000), Physica-Verlag, Heidelberg
22. Mangasarian O.L., and Wolberg W.H. Cancer diagnosis via linear programming. SIAM News 23 5 (1990) 1-18
23. Negnevitsky M. Artificial intelligence: a guide to intelligent systems (2002), Addison-Wesley, NJ
24. Odom, M.D., & Sharda, R. (1990). A neural network for bankruptcy prediction. In: Proceedings of The International Joint Conference on Neural Networks (vol. II) (pp. 163-168). San Diego, USA.
25. Pedrycz W., and Gomide F. An introduction to fuzzy sets: analysis and design (1998), MIT Press, Cambridge
26. Quinlan J.R. C4.5: programs for machine learning (1993), Morgan Kaufmann, San Mateo, CA
27. Rahimian E., and Singh S. Bankruptcy prediction by neural network. In: Trippi R.R., and Turban E. (Eds). Neural networks in finance and investing: Using artificial intelligence to improve real-world performance (1996), Irwin, Chicago 243-259
28. Ravi V., and Zimmermann H.-J. Fuzzy rule based classification with FeatureSelector and modified threshold accepting. European Journal of Operational Research 123 1 (2000) 16-28
29. Rooij A.J.F., Jain L.C., and Johnson R.P. Neural network training using genetic algorithms (1996), World Scientific, Singapore
30. Saaty T.L. The analytic hierarchy process: planning, priority setting, resource allocation (1980), McGraw-Hill, NY
31. Sharma S. Applied multivariate techniques (1996), John Wiley & Sons, Singapore
32. Zimmermann H.-J. Fuzzy set theory and its applications (1996), Kluwer, Boston