Abstract
One of the ways to solve classification problems with real-value attributes using a Learning Classifier System is the use of a discretization algorithm, which enables traditional discrete knowledge representations to solve these problems. A good discretization should balance losing the minimum of information and having a reasonable number of cut points. Choosing a single discretization that achieves this balance across several domains is not easy. This paper proposes a knowledge representation that uses several discretization (both uniform and non-uniform ones) at the same time, choosing the correct method for each problem and attribute through the iterations. Also, the intervals proposed by each discretization can split and merge among them along the evolutionary process, reducing the search space where possible and expanding it where necessary. The knowledge representation is tested across several domains which represent a broad range of possibilities.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Holland, J.H.: Adaptation in Natural and Artificial Systems. University of Michigan Press (1975)
Goldberg, D.E.: Genetic Algorithms in Search, Optimization and Machine Learning. Addison-Wesley Publishing Company, Inc. (1989)
DeJong, K.A., Spears, W.M.: Learning concept classification rules using genetic algorithms. Proceedings of the International Joint Conference on Artificial Intelligence (1991) 651–656
Wilson, S.W.: Classifier fitness based on accuracy. Evolutionary Computation 3 (1995) 149–175
Bacardit, J., Garrell, J.M.: Evolution of multi-adaptive discretization intervals for A rule-based genetic learning system. In: Proceedings of the VIII Iberoamerican Conference on Artificial Intelligence (IBERAMIA’2002), LNAI vol. 2527, Springer (2002) 350–360
Fayyad, U.M., Irani, K.B.: Multi-interval discretization of continuous-valued attributes for classification learning. In: IJCAI. (1993) 1022–1029
Wilson, S.W.: Get real! XCS with continuous-valued inputs. In Booker, L., Forrest, S., Mitchell, M., Riolo, R.L., eds.: Festschrift in Honor of John H. Holland, Center for the Study of Complex Systems (1999) 111–121
Liu, H., Setiono, R.: Chi2: Feature selection and discretization of numeric attributes. In: Proceedings of 7th IEEE International Conference on Tools with Artificial Intelligence, IEEE Computer Society (1995) 388–391
Aguilar-Ruiz, J.S., Riquelme, J.C., Valle, C.D.: Improving the evolutionary coding for machine learning tasks. In: Proceedings of the European Conference on Artificial Intelligence, ECAI’02, Lyon, France, IOS Press (2002) pp. 173–177
Giráldez, R., Aguilar-Ruiz, J.S., Riquelme, J.C.: Discretización supervisada no paramétrica orientada a la obtención de reglas de decisión. In: Proceedings of the CAEPIA2001. (2001) 53–62
Riquelme, J.C., Aguilar, J.S.: Codificación indexada de atributos continuos para algoritmos evolutivos en aprendizaje supervisado. In: Proceedings of the “Primer Congreso Español de Algoritmos Evolutivos y Bioinspirados (AEB’02)”. (2002) 161–167
Llorà, X., Garrell, J.M.: Knowledge-independent data mining with fine-grained parallel evolutionary algorithms. In: Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2001), Morgan Kaufmann (2001) 461–468
Rivest, R.L.: Learning decision lists. Machine Learning 2 (1987) 229–246
Soule, T., Foster, J.A.: Effects of code growth and parsimony pressure on populations in genetic programming. Evolutionary Computation 6 (1998) 293–309
Bacardit, J., Garrell, J.M.: Métodos de generalización para sistemas clasificadores de Pittsburgh. In: Proceedings of the “Primer Congreso Español de Algoritmos Evolutivos y Bioinspirados (AEB’02)”. (2002) 486–493
Blake, C., Keogh, E., Merz, C.: Uci repository of machine learning databases (1998) Blake, C., Keogh, E., & Merz, C.J. (1998). UCI repository of machine learning databases (www.ics.uci.edu/mlearn/MLRepository.html).
Martínez Marroquín, E., Vos, C., et al.: Morphological analysis of mammary biopsy images. In: Proceedings of the IEEE International Conference on Image Processing (ICIP’96). (1996) 943–947
Martí, J., Cufí, X., Regincós, J., et al.: Shape-based feature selection for microcalcification evaluation. In: Imaging Conference on Image Processing, 3338:1215–1224. (1998)
Golobardes, E., Llorà, X., Garrell, J.M., Vernet, D., Bacardit, J.: Genetic classifier system as a heuristic weighting method for a case-based classifier system. Butlletí de l’Associació Catalana d’Intel.ligència Artificial 22 (2000) 132–141
Witten, I.H., Frank, E.: Data Mining: practical machine learning tools and techniques with java implementations. Morgan Kaufmann (2000)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2003 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Bacardit, J., Garrell, J.M. (2003). Evolving Multiple Discretizations with Adaptive Intervals for a Pittsburgh Rule-Based Learning Classifier System. In: Cantú-Paz, E., et al. Genetic and Evolutionary Computation — GECCO 2003. GECCO 2003. Lecture Notes in Computer Science, vol 2724. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45110-2_80
Download citation
DOI: https://doi.org/10.1007/3-540-45110-2_80
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-40603-7
Online ISBN: 978-3-540-45110-5
eBook Packages: Springer Book Archive