Abstract
An interesting feature of encoding the individuals of a Pittsburgh learning classifier system as a decision list is the emergent generation of a default rule. However, performance of the system is strongly tied to the learning system choosing the correct class for this default rule. In this paper we experimentally study the use of an explicit (static) default rule. We first test simple policies for setting the class of the default rule, such as the majority/minority class of the problem. Next, we introduce some techniques to automatically determine the most suitable class.
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References
Holland, J.H.: Adaptation in Natural and Artificial Systems. University of Michigan Press, Ann Arbor (1975)
Goldberg, D.E.: Genetic Algorithms in Search, Optimization and Machine Learning. Addison-Wesley, Reading (1989)
DeJong, K.A., Spears, W.M., Gordon, D.F.: Using genetic algorithms for concept learning. Machine Learning 13(2/3), 161–188 (1993), citeseer.nj.nec.com/dejong93using.html
Rivest, R.L.: Learning decision lists. Machine Learning 2(3), 229–246 (1987), citeseer.nj.nec.com/rivest87learning.html
Janikow, C.: Indictive Learning of Decision Rules in Attribute-Based Examples: a Knowledge-Intensive Genetic Algorithm Approach. Phd dissertation, University of North Carolina (1991)
Quinlan, J.R.: C4.5: Programs for Machine Learning. Morgan Kaufmann, San Francisco (1993)
Cohen, W.W.: Fast effective rule induction. In: International Conference on Machine Learning, pp. 115–123 (1995), citeseer.nj.nec.com/cohen95fast.html
Bacardit, J.: Pittsburgh Genetics-Based Machine Learning in the Data Mining era: Representations, generalization, and run-time. PhD thesis, Ramon Llull University, Barcelona, Catalonia, Spain (2004)
Soule, T., Foster, J.A.: Effects of code growth and parsimony pressure on populations in genetic programming. Evolutionary Computation 6(4), 293–309 (1998), citeseer.nj.nec.com/316244.html
Rissanen, J.: Modeling by shortest data description. Automatica 14, 465–471 (1978)
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 6th International Workshop on Learning Classifier Systems, Springer, Heidelberg (in press, 2003)
Bacardit, J., Garrell, J.M.: Analysis and improvements of the adaptive discretization intervals knowledge representation. In: Deb, K., et al. (eds.) GECCO 2004. LNCS, vol. 3102, Springer, Heidelberg (2004)
Blake, C., Keogh, E., Merz, C.: UCI repository of machine learning databases (1998), http://www.ics.uci.edu/mlearn/MLRepository.html
Oei, C.K., Goldberg, D.E., Chang, S.J.: Tournament selection, niching, and the preservation of diversity. IlliGAL Report No. 91011, University of Illinois at Urbana-Champaign, Urbana, IL (1991)
Kohavi, R.: A study of cross-validation and bootstrap for accuracy estimation and model selection. In: IJCAI, pp. 1137–1145 (1995), citeseer.nj.nec.com/kohavi95study.html
Goldberg, D.E.: Sizing populations for serial and parallel genetic algorithms. In: Proceedings of the Third International Conference on Genetic Algorithms (ICGA89), pp. 70–79. Morgan Kaufmann, San Francisco (1989)
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Bacardit, J., Goldberg, D.E., Butz, M.V. (2007). Improving the Performance of a Pittsburgh Learning Classifier System Using a Default Rule. 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_20
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DOI: https://doi.org/10.1007/978-3-540-71231-2_20
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