Encyclopedia of Machine Learning and Data Mining

2017 Edition
| Editors: Claude Sammut, Geoffrey I. Webb

Classifier Systems

  • Pier Luca Lanzi
Reference work entry
DOI: https://doi.org/10.1007/978-1-4899-7687-1_941

Synonyms

Definition

Classifier systems are rule-based systems that combine  temporal difference learning or  supervised learning with a genetic algorithm to solve classification and  reinforcement learning problems. Classifier systems come in two flavors: Michigan classifier systems, which are designed for online learning, but can also tackle offline problems; and Pittsburgh classifier systems, which can only be applied to offline learning.

In Michigan classifier systems (Holland 1976), learning is viewed as an online adaptation process to an unknown environment that represents the problem and provides feedback in terms of a numerical reward. Michigan classifier systems maintain a single candidate solution consisting of a set of rules, or a population of classifiers. Michigan systems apply (1) temporal difference learning to distribute the incoming reward to the classifiers that are accountable for it; and (2) a genetic...

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Recommended Reading

  1. Arthur BW, Holland JH, LeBaron B, Palmer R, Talyer P (1996) Asset pricing under endogenous expectations in an artificial stock market. Technical report, Santa Fe InstituteGoogle Scholar
  2. Bacardit i Peñarroya J (2004) Pittsburgh genetic-based machine learning in the data mining era: representations, generalization, and run-time. PhD thesis, Computer Science Department, Enginyeria i Arquitectura La Salle Universitat Ramon Llull, BarcelonaGoogle Scholar
  3. Barry AM, Holmes J, Llora X (2004) Data mining using learning classifier systems. In: Bull L (ed) Applications of learning classifier systems, studies in fuzziness and soft computing, vol 150. Springer, Pagg, pp 15–67CrossRefGoogle Scholar
  4. Bassett JK, de Jong KA (2000) Evolving behaviors for cooperating agents. In: Proceedings of the twelfth international symposium on methodologies for intelligent systems. LNAI, vol 1932. Springer, BerlinGoogle Scholar
  5. Booker LB (1989) Triggered rule discovery in classifier systems. In: Schaffer JD (ed) Proceedings of the 3rd international conference on genetic algorithms (ICGA89). Morgan Kaufmann, San FranciscoGoogle Scholar
  6. Bull L (ed) (2004) Applications of learning classifier systems, studies in fuzziness and soft computing, vol 150. Springer, Berlin. ISBN 978-3-540-21109-9Google Scholar
  7. Bull L, Kovacs T (eds) (2005) Foundations of learning classifier systems, studies in fuzziness and soft computing, vol 183. Springer, Berlin. ISBN 978-3-540-25073-9Google Scholar
  8. Butz MV (2002) Anticipatory learning classifier systems. Genetic algorithms and evolutionary computation. Kluwer, Boston Academic Publishers.zbMATHCrossRefGoogle Scholar
  9. Clark P, Niblett T (1989) The CN2 induction algorithm. Mach Learn 3(4):261–283Google Scholar
  10. de Jong K (1988) Learning with genetic algorithms: an overview. Mach Learn 3(2–3):121–138Google Scholar
  11. de Jong KA, Spears WM (1991) Learning concept classification rules using genetic algorithms. In: Proceedings of the international joint conference on artificial intelligence. Morgan Kaufmann, San Francisco, pp 651–656Google Scholar
  12. Dorigo M, Bersini H (1994) A comparison of Q-learning and classifier systems. In: Cliff D, Husbands P, Meyer J-A, Wilson SW (eds) From animals to animats 3: proceedings of the third international conference on simulation of adaptive behavior. MIT Press, Cambridge, pp 248–255Google Scholar
  13. Dorigo M, Colombetti M (1998) Robot shaping: an experiment in behavior engineering. MIT Press/Bradford Books, CambridgeGoogle Scholar
  14. Goldberg DE (1989) Genetic algorithms in search, optimization, and machine learning. Addison-Wesley, ReadingzbMATHGoogle Scholar
  15. Grefenstette JJ, Ramsey CL, Schultz A (1990) Learning sequential decision rules using simulation models and competition. Mach Learn 5(4):355–381Google Scholar
  16. Holland J (1986) Escaping brittleness: the possibilities of general-purpose learning algorithms applied to parallel rule-based systems. In: Michalski RS, Carbonell JG, Mitchell TM (eds) Machine learning, an artificial intelligence approach, vol II, Chap. 20 Morgan Kaufmann, San Francisco, pp 593–623Google Scholar
  17. Holland JH (1975) Adaptation in natural and artificial systems. University of Michigan Press, Ann Arbor (Reprinted by the MIT Press in 1992)Google Scholar
  18. Holland JH (1976) Adaptation. Progress in theoretical biology 4:263–293CrossRefGoogle Scholar
  19. Holland JH, Reitman JS (1978) Cognitive systems based on adaptive algorithms. In: Waterman DA, Hayes-Roth F (eds) Pattern-directed inference systems. Academic Press, New York (Reprinted from Evolutionary computation. The fossil record. Fogel DB (ed.) IEEE Press (1998))Google Scholar
  20. Janikow CZ (1993) A knowledge-intensive genetic algorithm for supervised learning. Mach Learn 13(2–3):189–228CrossRefGoogle Scholar
  21. Lanzi PL (2001) Mining interesting knowledge from data with the XCS classifier system. In: Spector L, Goodman ED, Wu A, Langdon WB, Voigt H-M, Gen M et al (eds) Proceedings of the genetic and evolutionary computation conference (GECCO-2001). Morgan Kaufmann, San Francisco, pp 958–965Google Scholar
  22. Lanzi PL (2005) Learning classifier systems: a reinforcement learning perspective. In: Bull L, Kovacs T (eds) Foundations of learning classifier systems, studies in fuzziness and soft computing. Springer, Berlin, pp 267–284CrossRefGoogle Scholar
  23. Lanzi PL, Perrucci A (1999) Extending the representation of classifier conditions part II: from messy coding to S-expressions. In: Banzhaf W, Daida J, Eiben AE, Garzon MH, Honavar V, Jakiela M, Smith RE (eds) Proceedings of the genetic and evolutionary computation conference (GECCO 99). Morgan Kaufmann, Orlando, pp 345–352Google Scholar
  24. Lanzi PL, Riolo RL (2003) Recent trends in learning classifier systems research. In: Ghosh A, Tsutsui S (eds) Advances in evolutionary computing: theory and applications. Springer, Berlin, pp 955–988CrossRefGoogle Scholar
  25. Lanzi PL, Stolzmann W, Wilson SW (eds) (2000) Learning classifier systems: from foundations to applications. Lecture notes in computer science, vol 1813. Springer, BerlinGoogle Scholar
  26. Llorá X (2002) Genetics-based machine learning using fine-grained parallelism for data mining. PhD thesis, Enginyeria i Arquitectura La Salle, Ramon Llull University, BarcelonaGoogle Scholar
  27. Mellor D (2005) A first order logic classifier system. In: Beyer H (ed) Proceedings of the 2005 conference on genetic and evolutionary computation (GECCO ’05). ACM Press, New York, pp 1819–1826CrossRefGoogle Scholar
  28. Quinlan JR, Cameron-Jones RM (1995) Induction of logic programs: FOIL and related systems. New Gener Comput 13(3–4):287–312CrossRefGoogle Scholar
  29. Samuel AL (1959) Some studies in machine learning using the game of checkers. In: Feigenbaum, Feldman J (eds) Computers and thought. McGraw-Hill, New YorkGoogle Scholar
  30. Smith RE, Dike BA, Niehra RK, Ravichandran B, El-Fallah A (2000) Classifier systems in combat: two-sided learning of maneuvers for advanced fighter aircraft. Comput Methods Appl Mech Eng 186(2–4):421–437zbMATHCrossRefGoogle Scholar
  31. Smith SF (1980) A learning system based on genetic adaptive algorithms. Doctoral dissertation, Department of Computer Science, University of PittsburghGoogle Scholar
  32. Smith SF (1983) Flexible learning of problem solving heuristics through adaptive search. In: Proceedings of the eighth international joint conference on artificial intelligence. Morgan Kaufmann, Los Altos, pp 421–425Google Scholar
  33. Sutton RS (1988) Learning to predict by the methods of temporal differences. Mach Learn 3:9–44Google Scholar
  34. Sutton RS, Barto AG (1998) Reinforcement learning: an introduction. MIT Press, CambridgeGoogle Scholar
  35. Tackett WA (1994) Recombination, selection, and the genetic construction of computer programs. Unpublished doctoral dissertation, University of Southern CaliforniaGoogle Scholar
  36. Watkins C (1989) Learning from delayed rewards. PhD thesis, King’s CollegeGoogle Scholar
  37. Wilson SW (1995) Classifier fitness based on accuracy. Evol Comput 3(2):149–175CrossRefGoogle Scholar
  38. Wilson SW (2002) Classifiers that approximate functions. Natl Comput 1(2–3):211–234MathSciNetzbMATHCrossRefGoogle Scholar
  39. Wilson SW (2007). “Three architectures for continuous action” learning classifier systems. International workshops, IWLCS 2003–2005, revised selected papers. In: Kovacs T, Llorà X, Takadama K, Lanzi PL, Stolzmann W, Wilson SW (eds) Lecture notes in artificial intelligence, vol 4399. Springer, Berlin, pp 239–257Google Scholar

Copyright information

© Springer Science+Business Media New York 2017

Authors and Affiliations

  • Pier Luca Lanzi
    • 1
  1. 1.Politecnico di MilanoMilanoItaly