Learning classifier system ensembles with rule-sharing

IEEE Transactions on Evolutionary Computation

Volumn 11, Issue 4, 2007, Pages 496-502

  Bull, Larry ^a   Studley, Matthew ^a   Bagnall, Anthony ^b   Whittley, Ian ^b  

^a UNIVERSITY OF THE WEST OF ENGLAND   (United Kingdom)

^b UNIVERSITY OF EAST ANGLIA   (United Kingdom)

Author keywords

Data mining; Genetic algorithms (GAs); Parallel systems; Reinforcement learning

Indexed keywords

DATA MINING; GENETIC ALGORITHMS; PARALLEL ALGORITHMS; PARALLEL PROCESSING SYSTEMS; REINFORCEMENT LEARNING;

ACCURACY-BASED SYSTEMS; LEARNING CLASSIFIER SYSTEM; RULE MIGRATION MECHANISM;

LEARNING SYSTEMS;

EID: 34547896246     PISSN: 1089778X     EISSN: None     Source Type: Journal    
DOI: 10.1109/TEVC.2006.885163     Document Type: Article

References (41)

1. A. Bagnall and G. Smith, "Using an adaptive agent to bid in a simplified model of the UK market in electricity," in Proc. Genetic Evol. Comput. Conf., W. Banzhaf, Ed., Morgan Kaufmann, 1999, p. 74.
2. E. Bernadó, X. Llorà, and J. Garrell, P.-L. Lanzi, W. Stolzmann, and S. W. Wilson, Eds., "XCS and GALE: A comparative study of two learning classifier systems with six other learning algorithms on classification tasks," in Advances in Learning Classifier Systems - IWLCS 2001. Berlin, Germany: Springer-Verlag, 2002, pp. 115-133.
3. P. Bonelli and A. Parodi, R. Belew and L. Booker, Eds., "An efficient classifier system and its experimental comparison with two representative learning methods on three medical domains," in Proc. 4th Int. Conf. Genetic Algorithms, 1991, pp. 288-295.
4. G. Brown, J.Wyatt, R. Harris, and X.Yao, Diversity Creation Methods: A Survey and Categorisation. Amsterdam, The Netherlands: Elsevier Science, 2004.
5. L. Bull, "On using ZCS in a simulated continuous double-auction market," in Proc. Genetic Evol. Comput. Conf., W. Banzhaf, Ed., 1999, pp. 83-90.
6. L. Bull, "On accuracy-based fitness," Soft Computing, vol. 6, no. 3-4, pp. 154-161, 2002.
7. L. Bull, Applications of Learning Classifier Systems. Berlin, Germany: Springer-Verlag, 2004.
8. L. Bull, J. Sha'Aban, A. Tomlinson, P. Addison, and B. Heydecker, "Towards distributed adaptive control for road traffic junction signals using learning classifier systems," in Applications of Learning Classifier Systems, L. Bull, Ed. Berlin, Germany: Springer-Verlag, 2004, pp. 276-299.
9. L. Bull, "Two simple learning classifier systems," in Foundations of Learning Classifier Systems, L. Bull and T. Kovacs, Eds. Berlin, Germany: Springer-Verlag, 2005, pp. 63-90.
10. M. Butz, T. Kovacs, P. L. Lanzi, and S. W. Wilson, "Toward a theory of generalization and learning in XCS," IEEE Trans. Evol. Comput., vol. 8, no. 1, p. 28, Feb. 2004.
11. E. Cantu-Paz, Efficient and Accurate Parallel Genetic Algorithms. Norwell, MA: Kluwer, 2000.
12. E. Cantu-Paz and C. Kamath, "Inducing oblique decision trees with evolutionary algorithms," IEEE Trans. Evol. Comput., vol. 7, no. 1, pp. 54-68, Feb. 2003.
13. S. Celis and D. R. Musicant,Weka-Parallel Tech. Rep., 2003. [Online]. Available: http://weka-parallel.sourceforge.net/
14. J. Cohoon, S. Hegde, W. Martin, and D. Richards, "Punctuated equilibria: A parallel genetic algorithm," in Proc. 2nd Int. Conf. Genetic Algorithms, J. J. Grefenstette, Ed., 1987, pp. 148-154.
15. T. Dietterich, "An experimental comparison of three methods for constructing ensembles of decision trees: Bagging, boosting, and randomization," Mach. Learn., vol. 40, no. 2, pp. 139-158, 2000.
16. M. Dorigo and M. Colombetti, Robot Shaping. Cambridge, MA:MIT Press, 1997.
17. G. Folino, C. Pizzuti, and G. Spezzano, "Genetic programming and simulated annealing: A hybrid method to evolve decision trees," in Proc. 3rd Eur. Con. Genetic Program., 2000, pp. 294-303.
18. A. Freitas and S. H. Lavington, Mining Very Large Databases with Parallel Processing. Norwell, MA: Kluwer, 1998.
19. A. Greenver, "The use of a Learning Classifier System JXCS," CoIL Challenge 2000: The Insurance Company Case Leiden Institute of Advanced Computer Science, Tech. Rep. 2000-09, 2000, In P. van der Putten and M. van Someren, Eds..
20. L. Hercog and T. Fogarty, "Coevolutionary classifier systems for multi-agent simulation," in Proc. IEEE Congr. Evol. Comput., 2002, pp. 1789-1803.
21. T. Ho, J. J. Hull, and S. N. Srihari, "Decision combination in multiple classifier systems," IEEE Trans. on PAMI 16, no. 1, pp. 66-75, 1994.
22. J. H. Holland, Adaptation in Natural and Artificial Systems. Ann Arbor, MI: Univ. Michigan Press, 1975.
23. J. H. Holland, Progress in Theoretical Biology 4, R. Rosen and F. M. Snell, Eds. New York: Plenum, 1976, Adaptation.
24. J. H. Holland and J. Miller, "Artificially adaptive agents in economic theory," Amer. Econ. Rev., vol. 81, no. 2, pp. 365-370, 1991.
25. M. James, Classification Algorithms. New York: Wiley, 1985.
26. T. Kovacs, "Strength or accuracy? A comparison of two approaches to fitness calculation in learning classifier systems," in Learning Classifier Systems: From Foundations to Applications, P.-L. Lanzi, W. Stolzmann, and S. W. Wilson, Eds. Berlin, Germany: Springer-Verlag, 2000, pp. 194-208.
27. T. Kovacs, "Toward a theory of strong overgeneral classifiers," in Foundations of Genetic Algorithms 6, W. Martin and W. Spears, Eds. San Mateo, CA: Morgan Kaufmann, 2001, pp. 165-184.
28. L. Kunchevar and C. Whitaker, "Measures of diversity in classifier ensembles," Mach. Learn., vol. 51, pp. 181-207, 2003.
29. X. Llora, "Genetic based machine learning using fine-grained parallelism for data mining," Ph.D. dissertation, Ramon Llull Univ., Barcelona, Spain, 2002.
30. B. Manderick and P. Spiessens, "Fine-grained parallel genetic algorithms," in Proc. 3rd Int. Conf. Genetic Algorithms, J. D. Schaffer, Ed., 1989, pp. 428-433.
31. R. Marimon, E. McGrattan, and T. Sargent, "Money as a medium of exchange in an economy with artificially intelligent agents," Economic Dynamics and Control, vol. 14, pp. 329-373, 1990.
32. F. Neri and A. Giordana, "A parallel genetic algorithm for concept learning," in Proc. 6th Int. Conf. Genetic Algorithms, T. Baeck, Ed., 1995, pp. 436-443.
33. R. Palmer, W. Arthur, J. H. Holland, B. LeBaron, and P. Tayler, "Artificial economic life: A simple model of a stockmarket," Physica D, vol. 75, pp. 264-274, 1994.
34. J. R. Quinlan, C4.5: Programs for Machine Learning. San Mateo, CA: Morgan Kaufmann, 1993.
35. F. Seredynski, P. Cichosz, and G. Klebus, "Learning classifier systems in multi-agent environments," in Proc. 1st IEE/IEEE Conf. Genetic Algorithms in Eng. Syst.: Innovations Appl., 1995, pp. 287-292.
36. J. Shafer, R. Agrawal, and M. Mehta, "SPRINT: A scalable parallel classifier for data mining," in Proc. 22nd Very Large Data Bases Conf., 1996, pp. 78-84.
37. S. F. Smith, "A learning system based on genetic adaptive algorithms," Ph.D. dissertation, Univ. Pittsburgh, Pittsburgh, PA, 1980.
38. S. W. Wilson, "Classifier fitness based on accuracy," Evol. Comput. vol. 3, pp. 149-175, 1995.
39. S. W.Wilson, "Mining Oblique Data with XCS," in Proc. Advances in Learning Classifier Syst., P. L. Lanzi, W. Stolzmann, and S. W.Wilson, Eds., 2000, pp. 158-176.
40. M. J. Zaki and C.-T. Ho, Large-Scale Parallel Data Mining. Berlin, Germany: Springer-Verlag, 2002.
41. G. Robertson and R. Riolo, "A tale of two classifier systems," Mach. Learn., vol. 3, pp. 139-159, 1988.