Abstract
Genetic Based Machine Learning (GBML) systems traditionally have evolved rules that only deal with discrete attributes. Therefore, some discretization process is needed in order to teal with realvalued attributes.There are several methods to discretize real-valued attributes into a finite number of intervals, however none of them can efficiently solve all the possible problems.The alternative of a high number of simple uniform-width intervals usually expands the size of the search space without a clear performance gain.This paper proposes a rule representation which uses adaptive discrete intervals that split or merge through the evolution process, finding the correct discretization intervals at the same time as the learning process is done.
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References
Jaume Bacardit and Josep M. Garrell. Evolution of adaptive discretization intervals for a rule-based genetic learning system.In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2002) (to appear), 2002.
Jaume Bacardit and Josep M. Garrell. Métodos de generalización para sistemas clasificadores de Pittsburgh.In Proceedings of the “Primer Congreso Iberoamericano de Algoritmos Evolutivos y Bioinspirados (AEB’02)”, pages 486–493, 2002.
C. Blake, E. Keogh, and C. Merz. Uci repository of machine learning databases, 1998. Blake, C., Keogh, E., & Merz, C.J. (1998). UCI repository of machine learning databases (http://www.ics.uci.edu/mlearn/MLRepository.html).
A.L. Corcoran and S. Sen. Using real-valued genetic algorithms to evolve rule sets for classification.In Proceedings of the IEEE Conference on Evolutionary Computation, pages 120–124, 1994.
O. Cordón, M. del Jesus, and F. Herrera. Genetic learning of fuzzy rule-based classification systems co-operating with fuzzy reasoning methods.In International Journal of Intelligent Systems, Vol. 13 (10–11), pages 1025–1053, 1998.
Kenneth A. DeJong and William M. Spears. Learning concept classification rules using genetic algorithms. Proceedings of the International Joint Conference on Artificial Intelligence, pages 651–656, 1991.
Usama M. Fayyad and Keki B. Irani. Multi-interval discretization of continuousvalued attributes for classification learning.In IJCAI, pages 1022–1029, 1993.
David E. Goldberg. Genetic Algorithms in Search, Optimization and Machine Learning.Addison-W esley Publishing Company, Inc., 1989.
Elisab et Golobardes, Xavier Llorá, Josep Maria Garrell, David Vernet, and Jaume Bacardit.Genetic classifier system as a heuristic weighting method for a casebased classifier system. Butlletí de l’Associació Catalana d’Intel.ligéncia Artificial, 22:132–141, 2000.
John H. Holland. Adaptation in Natural and Artificial Systems.Univ ersity of Michigan Press, 1975.
John H. Holland. Escaping Brittleness: The possibilities of General-Purpose Learning Algorithms Applied to Parallel Rule-Based Systems.In Machine learning, an artificial intelligence approach. Volume II, pages 593–623.1986.
Ron Kohavi. A study of cross-validation and bootstrap for accuracy estimation and model selection.In IJCAI, pages 1137–1145, 1995.
Alexander V. Kozlov and Daphne Koller. Nonuniform dynamic discretization in hybrid networks.In Proceedings of the 13th Annual Conference on Uncertainty in AI (UAI), pages 314–325, 1997.
H. Liu and R. Setiono. Chi2: Feature selection and discretization of numeric attributes. In Proceedings of 7th IEEE International Conference on Tools with Artificial Intelligence, pages 388–391.IEEE Computer Society, 1995.
Xavier Llorá and Josep M. Garrell. Knowledge-independent data mining with fine-grained parallel evolutionary algorithms.In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2001), pages 461–468.Morgan Kaufmann, 2001.
J. Martí, X. Cufí, J. Regincós, and et al. Shap e-based feature selection for microcalcification evaluation.In Imaging Conference on Image Processing, 3338:1215–1224, 1998.
E. Martínez Marroquín, C. Vos, and et al. Morphological analysis of mammary biopsy images.In Proceedings of the IEEE International Conference on Image Processing (ICIP’96), pages 943–947, 1996.
José C. Riquelme and Jesús S. Aguilar. Codificación indexada de atributos continuos para algoritmos evolutivos en aprendizaje supervisado.In Proceedings of the “Primer Congreso Iberoamericano de Algoritmos Evolutivos y Bioinspirados (AEB’02)”, pages 161–167, 2002.
Ronald L. Rivest. Learning decision lists. Machine Learning, 2(3):229–246, 1987.
Stephen F. Smith. Flexible learning of problem solving heuristics through adaptive search.In Proceedings of the 8th International Joint Conference on Artificial Intelligence (IJCAI-83), pages 421–425, Los Altos, CA, 1983.Morgan Kaufmann.
Terence Soule and James A. Foster. Effects of code growth and parsimony pressure on populations in genetic programming. Evolutionary Computation, 6(4):293–309, Winter 1998.
Stewart W. Wilson.Get real! XCS with continuous-valued inputs.In L. Booker, Stephanie Forrest, M. Mitchell, and Rick L. Riolo, editors, Festschrift in Honor of John H. Holland, pages 111–121.Center for the Study of Complex Systems, 1999.
Ian H. Witten and Eibe Frank. Data Mining: practical machine learning tools and techniques with java implementations.Morgan Kaufmann, 2000.
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Bacardit, J., Maria Garrell, J. (2002). Evolution of Multi-adaptive Discretization Intervals for a Rule-Based Genetic Learning System. In: Garijo, F.J., Riquelme, J.C., Toro, M. (eds) Advances in Artificial Intelligence — IBERAMIA 2002. IBERAMIA 2002. Lecture Notes in Computer Science(), vol 2527. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-36131-6_36
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