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Alves, Roberto T.; Delgado, Myriam; Lopes, Heitor S.; Freitas, Alex A. (2004)
Publisher: Springer-Verlag
Languages: English
Types: Unknown
Subjects: QA76
This work proposes a classification-rule discovery algorithm integrating artificial immune systems and fuzzy systems. The algorithm consists of two parts: a sequential covering procedure and a rule evolution procedure. Each antibody (candidate solution) corresponds to a classification rule. The classification of new examples (antigens) considers not only the fitness of a fuzzy rule based on the entire training set, but also the affinity between the rule and the new example. This affinity must be greater than a threshold in order for the fuzzy rule to be activated, and it is proposed an adaptive procedure for computing this threshold for each rule. This paper reports results for the proposed algorithm in several data sets. Results are analyzed with respect to both predictive accuracy and rule set simplicity, and are compared with C4.5rules, a very popular data mining algorithm.
  • The results below are discovered through our pilot algorithms. Let us know how we are doing!

    • 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. Freitas, A.A., Timmis, J.: Revisiting the Foundations of Artificial Immune Systems: a Problem-oriented Perspective. Proc. 2nd 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
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