Advertisement

Mining Fuzzy Classification Rules Using an Artificial Immune System with Boosting

  • Bilal Alatas
  • Erhan Akin
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3631)

Abstract

In this study, a classification model including fuzzy system, artificial immune system (AIS), and boosting is proposed. The model is mainly focused on the clonal selection principle of biological immune system and evolves a population of antibodies, where each antibody represents the antecedent of a fuzzy classification rule while each antigen represents an instance. The fuzzy classification rules are mined in an incremental fashion, in that the AIS optimizes one rule at a time. The boosting mechanism that is used to increase the accuracy rates of the rules reduces the weight of training instances that are correctly classified by the new rule. Whenever AIS mines a rule, this rule is added to the mined rule list and mining of next rule focuses on rules that account for the currently uncovered or misclassified instances. The results obtained by proposed approach are analyzed with respect to predictive accuracy and simplicity and compared with C4.5Rules.

Keywords

Fuzzy Rule Class Label Artificial Immune System Training Instance Rule Pruning 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Han, J., Kamber, M.: Data Mining: Concepts and Techniques. Morgan Kaufmann Publishers Academic Press, San Francisco (2001)Google Scholar
  2. 2.
    Castro, L.N., Timmis, J.: Artificial Immune Systems: A New Computation Intelligence Approach. Springer, Berlin (2002)Google Scholar
  3. 3.
    de Castro, L.N., Von Zuben, F.J.: Learning and Optimization Using the Clonal Selection Principle. IEEE Transaction on Evolutionary Computation 6(3), 239–251 (2002)CrossRefGoogle Scholar
  4. 4.
    Freund, Y., Schapire, R.E.: Experiments with a New Boosting Algorithm. In: Proc. of the 13th Int. Conf. on Machine Learning ML 1996, pp. 148–156 (1996)Google Scholar
  5. 5.
    Gonzales, F.A., Dasgupta, D.: An Immunogenetic Technique to Detect Anomalies in Network Traffic. In: Proceedings of Genetic and Evolutionary Computation, pp. 1081–1088. Morgan Kaufmann, San Mateo (2002)Google Scholar
  6. 6.
    Freitas, A.A., Timmis, J.: Revisiting the Foundations of Artificial Immune Systems: a Problem Oriented Perspective. In: Timmis, J., Bentley, P.J., Hart, E. (eds.) ICARIS 2003. LNCS, vol. 2787, pp. 229–241. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  7. 7.
    Alves, R.T., Degado, M.R., Lopes, H.S., Freitas, A.A.: An Artificial Immune System for Fuzzy-Rule Induction in Data Mining. In: Yao, X., Burke, E.K., Lozano, J.A., Smith, J., Merelo-Guervós, J.J., Bullinaria, J.A., Rowe, J.E., Tiňo, P., Kabán, A., Schwefel, H.-P. (eds.) PPSN 2004. LNCS, vol. 3242, pp. 1011–1020. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  8. 8.
    Gonzáles, A., Herrera, F.: Multi-Stage Genetic Fuzzy Systems based on the Iterative Rule Learning Approach. Mathware & Soft Computing, 233–249 (1997)Google Scholar
  9. 9.
    Karcı, A.: Novelty in the Generation of Initial Population for Genetic Algorithms. Knowledge-Based Intelligent Information and Engineering Systems. In: Negoita, M.G., Howlett, R.J., Jain, L.C. (eds.) KES 2004. LNCS (LNAI), vol. 3214, pp. 268–276. Springer, Heidelberg (2004)Google Scholar
  10. 10.
    Alataş, B., Arslan, A.: Mining of Interesting Prediction Rules with Uniform Two-Level Genetic Algorithm. International Journal of Computational Intelligence 1(1), 65–70 (2004)Google Scholar
  11. 11.
    Back, T., Fogel, D.B., Michalewicz, T.: Evolutionary Computation, vol. 1. IoP Publishing, Oxford (2000)CrossRefGoogle Scholar
  12. 12.
    del Jesus, M.J., Hoffman, F., Navacués, L.J., Sánches, L.: Induction of Fuzzy-Rule-Based Classifiers with Evolutionary Boosting Algorithms. IEEE Transactions on Fuzzy Systems 12(3), 296–308 (2004)CrossRefGoogle Scholar
  13. 13.
    Quinlan, J.R.: C4.5: Programs For Machine Learning. Morgan Kaufmann, San Mateo (1993)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Bilal Alatas
    • 1
  • Erhan Akin
    • 1
  1. 1.Department of Computer Engineering, Faculty of EngineeringFirat UniversityElazigTurkey

Personalised recommendations