Abstract
In essence, data mining consists of extracting knowledge from data. This paper proposes a co-evolutionary system for discovering fuzzy classification rules. The system uses two evolutionary algorithms: a genetic programming (GP) algorithm evolving a population of fuzzy rule sets and a simple evolutionary algorithm evolving a population of membership function definitions. The two populations co-evolve, so that the final result of the co-evolutionary process is a fuzzy rule set and a set of membership function definitions which are well adapted to each other. In addition, our system also has some innovative ideas with respect to the encoding of GP individuals representing rule sets. The basic idea is that our individual encoding scheme incorporates several syntactical restrictions that facilitate the handling of rule sets in disjunctive normal form. We have also adapted GP operators to better work with the proposed individual encoding scheme.
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References
W. Banzhaf, P. Nordin, R.E. Keller, Francone FD Genetic Programming ∼ an Introduction. Morgan Kaufmann, 1998.
P.J. Bentley. “Evolutionary, my dear Watson”-investigating committee-based evolution of fuzzy rules for the detection of suspicious insurance claims. Proc. Genetic and Evolutionary Computation Conf. (GECCO-2000), 702–709. Morgan Kaufmann, 2000.
L.A. Breslow and D.W. Aha. Simplifying decision trees: a survey. The Knowledge Engineering Review, 12(1), 1–40. Mar. 1997.
M. Delgado, F.V. Zuben and F. Gomide. Modular and hierarchical evolutionary design of fuzzy systems. Proc. Genetic and Evolutionary Computation Conf. (GECCO-99), 180–187. Morgan Kaufmann, 1999.
U.M. Fayyad, G. Piatetsky-Shapiro and P. Smyth. From data mining to knowledge discovery: an overview. In: U.M. Fayyad et al. (Eds.) Advances in Knowledge Discovery & Data Mining, 1–34. AAAI/MIT, 1996.
C.S. Fertig, A.A. Freitas, L.V.R. Arruda and C. Kaestner. A Fuzzy Beam-Search Rule Induction Algorithm. Principles of Data Mining and Knowledge Discovery (Proc. 3rd European Conf.-PKDD-99). Lecture Notes in Artificial Intelligence 1704, 341–347. Springer-Verlag, 1999.
A.A. Freitas. A survey of evolutionary algorithms for data mining and knowledge discovery. To appear in: A. Ghosh and S. Tsutsui. (Eds.) Advances in Evolutionary Computation. Springer-Verlag, 2001.
A.A. Freitas and S.H. Lavington. Mining Very Large Databases with Parallel Processing. Kluwer Academic Publishers, 1998.
D.J. Hand. Construction and Assessment of Classification Rules. John Wiley&Sons, 1997.
H. Ishibuchi and T. Nakashima. Linguistic rule extraction by genetics-based machine learning. Proc. Genetic and Evolutionary Computation Conf. (GECCO-2000), 195–202. Morgan Kaufmann, 2000.
H. Ishibuchi, T. Nakashima and T. Kuroda. A hybrid fuzzy GBML algorithm for designing compact fuzzy rule-based classification systems. Proc. 9th IEEE Int. Conf. Fuzzy Systems (FUZZ IEEE 2000), 706–711. San Antonio, TX, USA. May 2000.
C.Z. Janikow. A knowledge-intensive genetic algorithm for supervised learning. Machine Learning 13, 189–228. 1993.
G.J. Klir and B. Yuan. Fuzzy Sets and Fuzzy Logic. Prentice-Hall, 1995.
W.B. Langdon, T. Soule, R. Poli and J.A. Foster. The evolution of size and shape. In: L. Spector, W.B. Langdon, U-M. O’Reilly and P.J. Angeline. (Eds.) Advances in Genetic Programming Volume 3, 163–190. MIT Press, 1999.
J.J. Liu and J.T. Kwok. An Extended Genetic Rule Induction Algorithm. Proc. Congress on Evolutionary Computation (CEC-2000). La Jolla, CA, USA. July 2000.
D.J. Montana. Strongly typed genetic programming. Evolutionary Computation 3(2), 199–230. 1995.
C.A. Pena-Reyes and M. Sipper. Designing breast cancer diagnostic systems via a hybrid fuzzy-genetic methodology. Proc. 8th IEEE Int. Conf. Fuzzy Systems. 1999.
S.E. Rouwhorst and A.P. Engelbrecht. Searching the Forest: Using Decision Tree as Building Blocks for Evolutionary Search in Classification. Proc. Congress on Evolutionary Computation (CEC-2000), 633–638. La Jolla, CA, USA. July 2000.
D. Walter and C.K. Mohan. ClaDia: a fuzzy classifier system for disease diagnosis. Proc. Congress on Evolutionary Computation (CEC-2000), 1429–1435. La Jolla, CA. 2000.
M.L. Wong and K.S. Leung. Data Mining Using Grammar Based Genetic Programming and Applications. Kluwer, 2000.
N. Xiong and L. Litz. Generating linguistic fuzzy rules for pattern classification with genetic algorithms. Principles of Data Mining and Knowledge Discovery (Proc. PKDD-99) Lecture Notes in Artificial Intelligence 1704, 574–579. Springer-Verlag, 1999.
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Mendes, R.R.F., de Voznika, F.B., Freitas, A.A., Nievola, J.C. (2001). Discovering Fuzzy Classification Rules with Genetic Programming and Co-evolution. In: De Raedt, L., Siebes, A. (eds) Principles of Data Mining and Knowledge Discovery. PKDD 2001. Lecture Notes in Computer Science(), vol 2168. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44794-6_26
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DOI: https://doi.org/10.1007/3-540-44794-6_26
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