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Evolution of Multi-adaptive Discretization Intervals for a Rule-Based Genetic Learning System

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Advances in Artificial Intelligence — IBERAMIA 2002 (IBERAMIA 2002)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 2527))

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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

  1. 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.

    Google Scholar 

  2. 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.

    Google Scholar 

  3. 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).

  4. 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.

    Google Scholar 

  5. 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.

    Article  Google Scholar 

  6. 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.

    Google Scholar 

  7. Usama M. Fayyad and Keki B. Irani. Multi-interval discretization of continuousvalued attributes for classification learning.In IJCAI, pages 1022–1029, 1993.

    Google Scholar 

  8. David E. Goldberg. Genetic Algorithms in Search, Optimization and Machine Learning.Addison-W esley Publishing Company, Inc., 1989.

    Google Scholar 

  9. 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.

    Google Scholar 

  10. John H. Holland. Adaptation in Natural and Artificial Systems.Univ ersity of Michigan Press, 1975.

    Google Scholar 

  11. 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.

    Google Scholar 

  12. Ron Kohavi. A study of cross-validation and bootstrap for accuracy estimation and model selection.In IJCAI, pages 1137–1145, 1995.

    Google Scholar 

  13. 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.

    Google Scholar 

  14. 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.

    Google Scholar 

  15. 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.

    Google Scholar 

  16. 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.

    Google Scholar 

  17. 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.

    Google Scholar 

  18. 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.

    Google Scholar 

  19. Ronald L. Rivest. Learning decision lists. Machine Learning, 2(3):229–246, 1987.

    Google Scholar 

  20. 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.

    Google Scholar 

  21. 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.

    Article  Google Scholar 

  22. 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.

    Google Scholar 

  23. Ian H. Witten and Eibe Frank. Data Mining: practical machine learning tools and techniques with java implementations.Morgan Kaufmann, 2000.

    Google Scholar 

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Author information

Authors and Affiliations

  1. Intelligent Systems Research Group Enginyeria i Arquitectura La Salle, Universitat Ramon Llull, Psg.Bonano va 8, 08022-Barcelona, Catalonia, Spain, Europe

    Jaume Bacardit & Josep Maria Garrell

Authors
  1. Jaume Bacardit
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  2. Josep Maria Garrell
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Editor information

Editors and Affiliations

  1. Telefónica Investigación y Desarrollo, Emilio Vargas 6, 28043, Madrid, Spain

    Francisco J. Garijo

  2. Dpto. Lenguajes y Sistemas Informáticos, Universidad de Sevilla, ETS Ingeniería Informática, 41012, Seville, Spain

    José C. Riquelme  & Miguel Toro  & 

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© 2002 Springer-Verlag Berlin Heidelberg

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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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  • DOI: https://doi.org/10.1007/3-540-36131-6_36

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-00131-7

  • Online ISBN: 978-3-540-36131-2

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