An artificial immune system for fuzzy-rule induction in data mining

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 3242, Issue , 2004, Pages 1011-1020

  Alves, Roberto T ^a   Delgado, Myriam R ^a   Lopes, Heitor S ^a   Freitas, Alex A ^b  

^a UNIVERSIDADE TECNOLÓGICA FEDERAL DO PARANÁ   (Brazil)

^b UNIVERSITY OF KENT   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; CLASSIFICATION (OF INFORMATION); FUZZY INFERENCE; FUZZY RULES; IMMUNE SYSTEM; PROBLEM SOLVING;

ADAPTIVE PROCEDURE; ARTIFICIAL IMMUNE SYSTEM; CLASSIFICATION RULE DISCOVERY; CLASSIFICATION RULES; DATA MINING ALGORITHM; FUZZY RULE BASED; FUZZY RULE INDUCTION; PREDICTIVE ACCURACY;

DATA MINING;

EID: 33645961388     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-540-30217-9_102     Document Type: Article

References (16)

1. Fayyad, U.M., Piatetsky-Shapiro, G., Smyth, P.: From Data Mining to Knowledge Discovery: an Overview. In: Fayyad, U.M. et al (Eds.) Advances in Knowledge Discovery and Data Mining. AAAI/MIT (1996) 1-34
2. Witten, I. H., Frank, E.: Data Mining: Pratical Machine Learning Tools and Techniques with Java Implementation. Morgan Kaufmann, San Mateo (2000)
3. Dasgupta, D.: Artificial Immune Systems and Their Applications. Springer, Berlin (1999)
4. Zadeh, L.A.: Fuzzy Sets. Inform. Control. 9 (1965) 338-352
5. Pedrycz, W., Gomide, F.: An Introduction to Fuzzy Sets. Analysis and Design. MIT Press, Cambridge (1998)
6. Castro, L.N., Timmis, J.: Artificial Immune Systems: A New Computation Intelligence Approach. Springer-Verlag, Berlin (2002)
7. Ishibuchi, H., Nakashima, T.: Effect of Rule Weights in Fuzzy Rule-based Classification Systems. IEEE T. Fuzzy Syst. 9:4 (2001) 506-515
8. Watkins, A.B., Boggess, L.C.: A Resource Limited Artificial Immune Classifier. Proc. Congress on Evolutionary Computation (2002) 926-931
9. Gonzales, F.A., Dasgupta, D.: An Immunogenetic Technique to Detect Anomalies in Network Traffic. Proceedings of Genetic and Evolutionary Computation. Morgan Kaufmann, San Mateo (2002) 1081-1088
10. nd International Conference on Artificial Immune Systems. Lecture Notes in Computer Science, Vol. 2787. Springer-Verlag, Berlin (2003) 229-241
11. Nasaroui, O., Gonzales, F., Dasgupta, D.: The Fuzzy Artificial Immune System: motivations, Basic Concepts, and Application to Clustering and Web Profiling. Proceedings of IEEE International Conference on Fuzzy Systems (2002) 711-716
12. Carvalho, D.R., Freitas, A.A.: A genetic Algorithm with Dequential Niching for Discovering Small-disjunct Rules. Proceedings of Genetic and Evolutionary Computation, Morgan Kaufmann, San Mateo (2002) 1035-1042
13. Back, T., Fogel, D.B., and Michalewicz, T. (Eds.): Evolutionary Computation, Vol. 1. IoP Publishing, Oxford, UK (2000)
14. Lopes, H.S., Coutinho, M.S., Lima, W.C.: An Evolutionary Approach to Simulate Cognitive Feedback Learning in Medical Domain. In: Sanchez, E., Shibata, T., Zadeh, L.A. (eds.), Genetic Algorithms and Fuzzy Logic Systems. World Scientific, Singapore (1997) 193-207
15. Quinlan, J.R.: C4.5: Programs For Machine Learning. Morgan Kaufmann, San Mateo, 1993
16. Parpinelli, R.S., Lopes, H.S., Freitas, A.A.: Data Mining With an Ant Colony Optimization Algorithm. IEEE T. Evol. Comput. 6:4 (2002) 321-332