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Learning Classifier Systems in Data Mining: An Introduction

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Book cover Learning Classifier Systems in Data Mining

Part of the book series: Studies in Computational Intelligence ((SCI,volume 125))

Summary

This chapter provides an introduction to Learning Classifier Systems before reviewing a number of historical uses in data mining. An overview of the rest of the volume is then presented.

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References

  1. Ahluwalia, M. & Bull, L. (1999) Coevolving functions in genetic programming: classification using K-nearest-neighbour. In: W. Banzhaf, J. Daida, A.E. Eiben, M.H. Garzon, V. Honavar, M. Jakiela & R.E. Smith (eds) GECCO-99: Proceedings of the Genetic and Evolutionary Computation Conference, pp. 947–952. Morgan Kaufmann, Los Altos, CA

    Google Scholar 

  2. Bernadó, E. Llora, X. & Garrell, J.M. (2002) XCS and GALE: a comparative study of two learning classifier systems on data mining. In: P.L. Lanzi, W. Stolzmann & S.W. Wilson (eds) Advances in Learning Classifier Systems, pp. 115–132, LNAI 2321. Springer, Berlin Heidelberg New York

    Google Scholar 

  3. Bonelli, P. & Parodi, A. (1991) An efficient classifier system and its experimental comparison with two representative learning methods on three medical domains. In: R.K. Belew & L.B. Booker (eds) Proceedings of the 4th International Conference on Genetic Algorithms, pp. 288–295. Morgan Kaufmann, Los Altos, CA

    Google Scholar 

  4. Booker, L. (1982) Intelligent Behavior as an Adaptation to the Task Environment. Ph.D. Thesis, the University of Michigan, USA

    Google Scholar 

  5. Booker, L. (1989) Triggered rule discovery in classifier systems. In: J.D. Schaffer (ed.) Proceedings of the Third International Conference on Genetic Algorithms, pp. 265–274. Morgan Kaufmann, Los Altos, CA

    Google Scholar 

  6. Bull, L. (2004) (ed.) Applications of Learning Classifier Systems. Springer, Berlin Heidelberg New York

    MATH  Google Scholar 

  7. Bull, L. & Hurst, J. (2002) ZCS redux. Evolutionary Computation 10(2): 185–205

    Article  Google Scholar 

  8. Bull, L. & Kovacs, T. (2005) (eds) Foundations of Learning Classifier Systems. Springer, Berlin Heidelberg New York

    MATH  Google Scholar 

  9. Bull, L. & O’Hara, T. (2002) Accuracy-based neuro and neuro-fuzzy classifier systems. In: W.B. Langdon, E. Cantu-Paz, K. Mathias, R. Roy, D. Davis, R. Poli, K. Balakrishnan, V. Honavar, G. Rudolph, J. Wegener, L. Bull, M.A. Potter, A.C. Schultz, J.F. Miller, E. Burke & N. Jonoska (eds) GECCO-2002: Proceedings of the Genetic and Evolutionary Computation Conference, pp. 905–911. Morgan Kaufmann, Los Altos, CA

    Google Scholar 

  10. Butz, M. (2005) Rule-Based Evolutionary Online Learning Systems. Springer, Berlin Heidelberg New York

    Google Scholar 

  11. Butz, M. & Wilson, S.W. (2001) An algorithmic description of XCS. In: P.L. Lanzi, W. Stolzmann & S.W. Wilson (eds) Advances in Learning Classifier Systems: Proceedings of the Third International Conference – IWLCS2000, pp. 253–272. Springer, Berlin Heidelberg New York

    Chapter  Google Scholar 

  12. Clark, P. & Niblett, P. (1987) Induction in noisy domains. In: I. Bratko & N. Lavrac (eds) Progress in Machine Learning, pp. 11–30. Sigma, Bled, Yugoslavia

    Google Scholar 

  13. Cliff, D. & Ross, S. (1995) Adding temporary memory to ZCS. Adaptive Behavior 3(2): 101–150

    Article  Google Scholar 

  14. Cordon, O. Herrera, F. & Sanchez, L. (1999) Solving electrical distribution problems using hybrid evolutionary data analysis techniques. Applied Intelligence 10(1): 5–24

    Article  Google Scholar 

  15. DeJong, K. Spears, W. & Gordon, D. (1993) Using genetic algorithms for concept learning. Machine Learning 13: 161–188

    Article  Google Scholar 

  16. Eiben, A. & Smith, J. (2003) Introduction to Evolutionary Computing. Springer, Berlin Heidelberg New York

    MATH  Google Scholar 

  17. Federman, F. & Dorchak, S.F. (1997) Information theory and NEXTPITCH, a learning classifier system. In: T. Baeck (ed.) Proceedings of the Seventh International Conference on Genetic Algorithms, pp. 442–448. Morgan Kaufmann, Los Altos, CA

    Google Scholar 

  18. Forrest, S. & Miller, J.H. (1991) Emergent behaviour in classifier systems. In: S. Forrest (ed.) Emergent Computation, pp. 213–227. MIT Press, Cambridge, MA

    Google Scholar 

  19. Frey, P. & Slate, D. (1991) Letter recognition using Holland-style adaptive classifiers. Machine Learning 6: 161–182

    Google Scholar 

  20. Giordana, A. & Neri, F. (1995) Search-intensive concept induction. Evolutionary Computation 3: 375–416

    Article  Google Scholar 

  21. Goldberg, D.E. (1983) Computer-aided gas pipeline operation using genetic algorithms and rule-learning. Ph.D. Thesis, University of Michigan

    Google Scholar 

  22. Goldberg, D.E. (1989) Genetic Algorithms in Search, Optimization, and Machine Learning. Addison-Wesley, Reading, MA

    MATH  Google Scholar 

  23. Goodloe, M. & Graves, S.J. (1988) Improving performance of an electric power expert system with genetic algorithms. In: Proceedings of the 1st International Conference on the Applications of Artificial Intelligence and Expert Systems. IEA/AIE-88, pp. 298–305

    Google Scholar 

  24. Greene, D.P. & Smith, S.F. (1994) Using coverage as a model building constraint in learning classifier systems. Evolutionary Computation 2(1): 67–91

    Article  Google Scholar 

  25. Hartley, A. (1999) Accuracy-based fitness allows similar performance to humans in static and dynamic classification. In: W. Banzhaf, J. Daida, A.E. Eiben, M.H. Garzon, V. Honavar, M. Jakiela & R.E. Smith (eds) GECCO-99: Proceedings of the Genetic and Evolutionary Computation Conference, pp. 266–273. Morgan Kaufmann, Los Altos, CA

    Google Scholar 

  26. Ho, T. Hull, J.J. & Srihari, S.N. (1994) Decision combination in multiple classifier systems. IEEE Trans on PAMI 16(1): 66–75

    Google Scholar 

  27. Holland, J.H. (1975) Adaptation in Natural and Artificial Systems. University of Michigan Press, Ann Arbor

    Google Scholar 

  28. Holland, J.H. (1976) Adaptation. In: R. Rosen & F.M. Snell (eds) Progress in Theoretical Biology, 4. Plenum, New York

    Google Scholar 

  29. Holland, J.H. (1980) Adaptive algorithms for discovering and using general patterns in growing knowledge bases. International Journal of Policy Analysis and Information Systems 4(3): 245–268

    Google Scholar 

  30. Holland, J.H. (1986). Escaping brittleness: the possibilities of general-purpose learning algorithms applied to parallel rule-based systems. In: R.S. Michalski, J.G. Carbonell & T.M. Mitchell (eds) Machine learning, an artificial intelligence approach. Morgan Kaufmann, Los Altos, CA

    Google Scholar 

  31. Holland, J.H. & Reitman, J.H. (1978) Cognitive systems based in adaptive algorithms. In: D.A. Waterman & F. Hayes-Roth (eds) Pattern-Directed Inference Systems. Academic, New York

    Google Scholar 

  32. Holland, J.H. Holyoak, K.J. Nisbett, R.E. & Thagard, P.R. (1986) Induction: Processes of Inference, Learning and Discovery. MIT Press, Cambridge, MA

    Google Scholar 

  33. Kovacs, T. (1997) XCS classifier system reliably evolves accurate, complete and minimal representations for Boolean functions. In: R. Roy, P. Chawdhry & R. Pant (eds) Soft Computing in Engineering Design and Manufacturing, pp. 59–68. Springer, Berlin Heidelberg New York

    Google Scholar 

  34. Koza, J.R. (1994) Genetic Programming. MIT Press, Cambridge, MA

    MATH  Google Scholar 

  35. Lavarac, N. & Dzeroski, S. (1994) Inductive Logic Programming: Techniques and Applications. Ellis Horwood, Chichester, UK

    Google Scholar 

  36. Marimon, R. McGrattan, E. & Sargent, J. (1990) Money as a medium of exchange in an economy with artificially intelligent agents. Journal of Economic Dynamics and Control 14: 329–373

    Article  MATH  MathSciNet  Google Scholar 

  37. O’Hara, T. & Bull, L. (2005) A memetic accuracy-based neural learning classifier system, Proceedings of the IEEE Congress on Evolutionary Computation, pp. 2040–2045. IEEE, New York

    Google Scholar 

  38. Pena-Reyes, C. & Sipper, M. (1999) A fuzzy-genetic approach to breast cancer diagnosis. Artificial Intelligence in Medicine 17(2): 155

    Article  Google Scholar 

  39. Quinlan, J.R. (1986) Induction of decision trees. Machine Learning 1: 18–106

    Google Scholar 

  40. Riolo, R. (1991) Modeling simple human category learning with a classifier system. In: L. Booker & R.K. Belew (eds) Proceedings of the Fourth International Conference on Genetic Algorithms, pp. 324–333. Morgan Kaufmann, Los Altos, CA

    Google Scholar 

  41. Robertson, G. & Riolo, R. (1988) A tale of two classifier systems. Machine Learning 3: 139–159

    Google Scholar 

  42. Samuel, A.L. (1959) Some studies in machine learning using the game of checkers. IBM Journal of Research and Development 3: 211–229

    Article  MathSciNet  Google Scholar 

  43. Saxon, S. & Barry, A. (2000) XCS and the Monk’s problems. In: P.-L. Lanzi, W. Stolzmann & S.W. Wilson (eds) Learning Classifier Systems: From Foundations to Applications, pp. 223–242. Springer, Berlin Heidelberg New York

    Chapter  Google Scholar 

  44. Sen, S. (1993) Improving classification accuracy through performance history. In: S. Forrest (ed.) Proceedings of the Seventh International Conference on Genetic Algorithms, p. 652. Morgan Kaufmann, Los Altos, CA

    Google Scholar 

  45. Smith, S.F. (1980) A Learning System Based on Genetic Adaptive Algorithms. Ph.D. Thesis, Univ. Pittsburgh, USA

    Google Scholar 

  46. Sutton, R. & Barto, A. (1998) Reinforcement Learning. MIT Press, Cambridge, MA

    Google Scholar 

  47. Valenzuela-Rendon, M. (1991) The fuzzy classifier system: a classifier system for continuously varying variables. In: Proceedings of the Fourth International Conference on Genetic Algorithms, pp. 346–353. Morgan Kaufmann, Los Altos, CA

    Google Scholar 

  48. Watkins, C.J. (1989) Learning from Delayed Rewards. Ph.D. Thesis, Cambridge University,USA

    Google Scholar 

  49. Wilson, S.W. (1987) Classifier systems and the animat problem. Machine Learning 2: 199–228

    Google Scholar 

  50. Wilson, S.W. (1994) ZCS: a zeroth-level classifier system. Evolutionary Computation 2(1): 1–18

    Article  Google Scholar 

  51. Wilson, S.W. (1995) Classifier fitness based on accuracy. Evolutionary Computation 3(2): 149–176

    Article  Google Scholar 

  52. Wilson, S.W. (1998) Generalization in the XCS classifier system. In: Koza et al. (eds) Genetic Programming 1998: Proceedings of the Third Annual Conference, pp. 322–334. Morgan Kaufmann, Los Altos, USA

    Google Scholar 

  53. Wilson, S.W. (2001) Function approximation with a classifier system. In: W.B. Langdon, E. Cantu-Paz, K. Mathias, R. Roy, D. Davis, R. Poli, K. Balakrishnan, V. Honavar, G. Rudolph, J. Wegener, L. Bull, M.A. Potter, A.C. Schultz, J.F. Miller, E. Burke & N. Jonoska (eds) GECCO-2001: Proceedings of the Genetic and Evolutionary Computation Conference, pp. 974–981. Morgan Kaufmann, Los Altos, CA

    Google Scholar 

  54. Wilson, S.W. & Goldberg, D.E. (1989) A critical review of classifier systems. In: Proceedings of the 3rd International Conference on Genetic Algorithms, pp. 244–255. Morgan Kaufmann, Los Altos, CA

    Google Scholar 

  55. Wyatt, D. & Bull, L. (2004) A memetic learning classifier system for describing continuous-valued problem spaces. In: N. Krasnagor, W. Hart & J. Smith (eds) Recent Advances in Memetic Algorithms, pp. 355–396. Springer, Berlin Heidelberg New York

    Google Scholar 

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Bull, L., Bernadó-Mansilla, E., Holmes, J. (2008). Learning Classifier Systems in Data Mining: An Introduction. In: Bull, L., Bernadó-Mansilla, E., Holmes, J. (eds) Learning Classifier Systems in Data Mining. Studies in Computational Intelligence, vol 125. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-78979-6_1

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  • DOI: https://doi.org/10.1007/978-3-540-78979-6_1

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-78978-9

  • Online ISBN: 978-3-540-78979-6

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