Abstract
Clonal selection principle based CLONALG is one of the most popular artificial immune system (AIS) models. It has been proposed to perform pattern matching and optimization task but has not been applied for classification tasks. Some work has been reported that accommodates CLONALG for classification but generally they do not perform well. This paper proposes an approach for classification using CLONALG with competitive results in terms of classification accuracy, compared to other AIS models and evolutionary algorithms tested on the same benchmark data sets. We named our algorithm CLONAX.
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References
Bacardit, J., Garrell, J.M.: Bloat control and generalization pressure using the minimum description length principle for a pittsburgh approach learning classifier system. In: Proceedings of the 2003-2005 International Conference on Learning Classifier Systems, pp. 59–79. Springer, Heidelberg (2007)
Bernadó-Mansilla, E., Garrell-Guiu, J.M.: Accuracy-Based Learning Classifier Systems: Models, Analysis and Applications to Classification Tasks. Evolutionary Computation 11(3), 209–238 (2003)
Butz, M.V., et al.: Toward a theory of generalization and learning in XCS. IEEE Transactions on Evolutionary Computation 8(1), 28–46 (2004)
Carter, J.H.: The Immune System as a Model for Pattern Recognition and Classification. Journal of the American Medical Informatics Association 7(1), 28–41 (2000)
de Castro, L.N., Von Zuben, F.J.: Learning and optimization using the clonal selection principle. IEEE Transactions on Evolutionary Computation 6(3), 239–251 (2002)
Dasgupta, D.: Advances in artificial immune systems. IEEE Computational Intelligence Magazine 1(4), 40–49 (2006)
Dasgupta, D., et al.: Artificial immune system (AIS) research in the last five years. In: The 2003 Congress on Evolutionary Computation, CEC 2003, vol. 1, pp. 123–130 (2003)
De Castro, L.N., Von Zuben, F.J.: The Clonal Selection Algorithm with Engineering Applications. In: GECCO 2002 - Workshop Proceedings, pp. 36–37 (2000)
Forrest, S., et al.: Self-nonself discrimination in a computer. In: Proceedings of IEEE Computer Society Symposium on Research in Security and Privacy, pp. 202–212 (1994)
Forrest, S., et al.: Using genetic algorithms to explore pattern recognition in the immune system. Evol. Comput. 1, 191–211 (1993)
Frank, A., Asuncion, A.: UCI Machine Learning Repository. University of California, Irvine, School of Information and Computer Sciences (2010)
Gonzblez, A., Perez, R.: SLAVE: a genetic learning system based on an iterative approach. IEEE Transactions on Fuzzy Systems 7(2), 176–191 (1999)
Hofmeyr, S.A.: An immunological model of distributed detection and its application to computer security. The University of New Mexico (1999)
Hofmeyr, S., Forrest, S.: Immunity by Design: An Artificial Immune System (1999)
Hunt, J., et al.: Jisys: Development of an Artificial Immune System for real world applications. In: Artificial Immune Systems and their Applications, pp. 157–186. Springer, Heidelberg (1998)
Ishibuchi, H., et al.: Performance evaluation of fuzzy classifier systems for multidimensional pattern classification problems. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, 29(5), 601–618 (1999)
Brownlee, J.: Clonal selection theory & CLONALG - The Clonal selection classification algorithm (CSCA). Swinburne University of Technology (2005)
White, J.A., Garrett, S.M.: Improved Pattern Recognition with Artificial Clonal Selection? In: Timmis, J., Bentley, P.J., Hart, E. (eds.) ICARIS 2003. LNCS, vol. 2787, pp. 181–193. Springer, Heidelberg (2003)
Jerne, N.K.: Towards a network theory of the immune system. Ann. Immunol (Paris) 125C(1-2), 373–389 (1974)
Ma, W., Tran, D., Sharma, D.: Negative selection with antigen feedback in intrusion detection. In: Bentley, P.J., Lee, D., Jung, S. (eds.) ICARIS 2008. LNCS, vol. 5132, pp. 200–209. Springer, Heidelberg (2008)
Otero, F.E.B., Freitas, A.A., Johnson, C.G.: cAnt-Miner: An Ant Colony Classification Algorithm to Cope with Continuous Attributes. In: Dorigo, M., Birattari, M., Blum, C., Clerc, M., Stützle, T., Winfield, A.F.T. (eds.) ANTS 2008. LNCS, vol. 5217, pp. 48–59. Springer, Heidelberg (2008)
Tanwani, A.K., Farooq, M.: Performance evaluation of evolutionary algorithms in classification of biomedical datasets. In: Proceedings of the 11th Annual Conference Companion on Genetic and Evolutionary Computation Conference: Late Breaking Papers, pp. 2617–2624 (2009)
Timmis, J., et al.: An Artificial Immune System for Data Analysis. Biosystems 55(1/3), 143–150 (2000)
Watkins, A., et al.: Artificial Immune Recognition System (AIRS): An Immune-Inspired Supervised Learning Algorithm. Genetic Programming and Evolvable Machines 5(3), 291–317 (2004)
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Sharma, A., Sharma, D. (2011). Clonal Selection Algorithm for Classification. In: Liò, P., Nicosia, G., Stibor, T. (eds) Artificial Immune Systems. ICARIS 2011. Lecture Notes in Computer Science, vol 6825. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-22371-6_31
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DOI: https://doi.org/10.1007/978-3-642-22371-6_31
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