Abstract
This paper proposes LCSE, a learning classifier system ensemble, which is an extension to the classical learning classifier system(LCS). The classical LCS includes two major modules, a genetic algorithm module used to facilitate rule discovery, and a reinforcement learning module used to adjust the strength of the corresponding rules after the learning module receives the rewards from the environment. In LCSE we build a two-level ensemble architecture to enhance the generalization of LCS. In the first-level, new instances are first bootstrapped and sent to several LCSs for classification. Then, in the second-level, a simple plurality-vote method is used to combine the classification results of individual LCSs into a final decision. Experiments on some benchmark medical data sets from the UCI repository have shown that LCSE has better performance on incremental medical data learning and better generalization ability than the single LCS and other supervised learning methods.
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References
Bonelli, P., Parodi, A.: An Efficient Classifier System and Its Experimental Comparison with Two Representative Learning Methods on Three Medical Domains. In: Belew, R.K., Booker, L.B. (eds.) Proceedings of the fourth international conference on Genetic algorithms (ICGA-4), pp. 288–295. Morgan Kaufmann, San Mateo (1991)
Holmes, J.H.: Learning Classifier Systems Applied to Knowledge Discovery in Clinical Research Databases. In: Lanzi, P.L., Stolzmann, W., Wilson, S.W. (eds.) IWLCS 1999. LNCS (LNAI), vol. 1813, pp. 243–261. Springer, Heidelberg (2000)
Holmes, J.H.: Applying a Learning Classifier System to Mining Explanatory and Predictive Models from a Large Database. In: Lanzi, P.L., Stolzmann, W., Wilson, S.W. (eds.) IWLCS 2000. LNCS (LNAI), vol. 1996, pp. 103–113. Springer, Heidelberg (2001)
Wilson, S.W.: Get Real! XCS with continous-valued inputs. In: Lanzi, P.L., Stolzmann, W., Wilson, S.W. (eds.) IWLCS 1999. LNCS (LNAI), vol. 1813, pp. 209–219. Springer, Heidelberg (2000)
Wilson, S.W.: Mining Obilque Data with XCS. In: Lanzi, P.L., Stolzmann, W., Wilson, S.W. (eds.) IWLCS 2000. LNCS (LNAI), vol. 1996, pp. 158–174. Springer, Heidelberg (2001)
Bernadó, E., Llorà , X., Garrell, J.M.: XCS and GALE: A Comparative Study of Two Learning Classifier Systems on Data Mining. In: Lanzi, P.L., Stolzmann, W., Wilson, S.W. (eds.) IWLCS 2001. LNCS (LNAI), vol. 2321, pp. 115–132. Springer, Heidelberg (2002)
Bacardit, J., Butz, M.V.: Data Mining in Learning Classifier Systems: Comparing XCS with GAssist. In: Kovacs, T., et al. (eds.) IWLCS 2003. LNCS (LNAI), vol. 4399, Springer, Heidelberg (2007)
Dietterich, T.G.: Ensemble Learning. In: The Handbook of Brain Theory and Neural Networks, 2nd edn., pp. 405–408. MIT Press, Cambridge (2002)
Breiman, L.: Bagging Predictors. Machine Learning 24(2), 123–140 (1996)
Zhou, Z.-H., Wu, J., Tang, W.: Ensembling neural networks: many could be better than all. Artificial Intelligence 137(1-2), 239–263 (2002)
Booker, L., Goldberg, D.E., Holland, J.H.: Classifier systems and genetic algorithms. Artificial Intelligence 40(1-3), 235–282 (1989)
Freund, Y., Schapire, R.: A short introduction to boosting. Journal of Japanese Society for Artificial Intelligence 14(5), 771–780 (1999)
Bauer, E., Kohavi, R.: An empirical comparison of voting classification algorithms: Bagging, boosting, and variants. Machine Learning 36(1-2), 105–139 (1999)
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Gao, Y., Huang, J.Z., Rong, H., Gu, Dq. (2007). LCSE: Learning Classifier System Ensemble for Incremental Medical Instances. In: Kovacs, T., Llorà , X., Takadama, K., Lanzi, P.L., Stolzmann, W., Wilson, S.W. (eds) Learning Classifier Systems. IWLCS IWLCS IWLCS 2003 2004 2005. Lecture Notes in Computer Science(), vol 4399. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-71231-2_7
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DOI: https://doi.org/10.1007/978-3-540-71231-2_7
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