Abstract
Wilson’s XCS classifier system has recently been modified and extended in ways which enable it to be applied to real-world benchmark data mining problems. Excellent results have been reported already on one such problem by Wilson, while other work by Saxon and Barry on a tunable collection of machine learning problems has also pointed to the strong potential of XCS in this area. In this paper we test a modified XCS implementation on twelve benchmark machine learning problems, all real-world derived. XCS is compared on these benchmarks with C4.5 and with HIDER (a new and sophisticated GA for machine learning developed elsewhere). Results for both C4.5, HIDER and XCS on each problem were tenfold cross-validated, and in the case of HIDER and XCS a modest amount of preliminary parameter investigation was done to find good results in each case. We find that XCS outperforms the other techniques in eight of the twelve problems, and is second-best in two of the remaining three. Some investigation is then done of the variance in XCS performance, and we find this to be verging on significant, either when varying the data fold composition, or the algorithmic random seed. We also investigate variation of several XCS parameters around well-known default settings. We find the default settings to be generally robust, but find the mutation rates and GA selection scheme to be particularly worthy of exploration with a view to improved performance. We conclude that XCS has the potential to be a powerful general data mining tool, at least for databases without too many fields, but that considerable research is warranted to identify rules and guidelines for parameter and strategy setting.
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
Aeberhard, S., Coomans, D., and de Vel, O. (1992) Comparison of Classifiers in High Dimensional Settings, Tech. Rep. no. 92-02, Dept. of Computer Science and Dept. of Mathematics and Statistics, James Cook University of North Queensland.
Aguilar, J.S., Riquelme, J.C., and Toro, M. (1998a) Decision Queue Classifier for Supervised Learning using Rotated Hyperboxes, in Progress in Artificial Intelligence: IBERAMIA’ 98, Springer LNAI volume 1484, pp. 326–335.
Aguilar, J.S., Riquelme, J.C., and Toro, M. (1998b) A Tool to Obtain a Hierarchical Qualitative Set of Rules from Quantitative Data, in Progress in Artificial Intelligence: IBERAMIA’ 98, Springer LNAI volume 1484, pp. 336–345.
Aguilar, J.S., Riquelme, J.C., and Toro, M. (2001) ‘Evolutionary Learning of Hierarchical Decision Rules’ paper submitted to the IEEE Transactions on Evolutionary Computation.
Blake, C.L. and Merz, C.J. (1998). UCI Repository of machine learning databases [http://www.ics.uci.edu/~mlearn/MLRepository.html]. Irvine, CA: University of California, Department of Information and Computer Science.
Bull, L. Hurst, J., and Tomlinson, A. (2000) Self-Adaptive Mutation in Classifier System Controllers. In J-A. Meyer, A. Berthoz, D. Floreano, H. Roitblatt, and S.W. Wilson (eds) From Animals to Animats 6-The Sixth International Conference on the Simulation of Adaptive Behaviour, MIT Press.
Butz, M.V. and Wilson, S.W. (2000) YEAR, ‘An Algorithmic Description of XCS’, Technical Report 2000017, Illinois Genetic Algorithms Laboratory, IL, USA.
Cendrowska, J. (1987) PRISM: An algorithm for inducing modular rules, International Journal of Man-Machine Studies, 27: 349–370
Diaconis, P. and Efron, B. (1983). Computer-Intensive Methods in Statistics. Scientific American, 248.
Evett, I.W. and Spiehler, E.J. (1987) Technical Note, Central Research Establishment Home Office Forensic Science Service Aldermaston, Reading, Berkshire, UK RG7 4PN
Fisher, R.A. (1936) The use of multiple measurements in taxonomic problems, Annual Eugenics, 7, Part II, 179–188 (1936)
Holland, J.H. (1976) Adaptation, in Rosen, R., and Snell, F.M. (eds.), Progress in Theoretical Biology, New York: Plenum.
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.
Holland, J.H., Booker, L.B., Colombetti, M., Dorigo, M., Goldberg, D.E., Forrest, S., Riolo, R.L., Smith, R.E., Lanzi, P.L., Stolzmann, W., and Wilson, S.W. (2000) What is a Learning Classifier System? In Lanzi, P.L., Stolzmann, W., and Wilson, S.W. (eds.), Learning Classifier Systems: From Foundations to Applications, Springer Lecture Notes in Computer Science 1813, pp. 3–32.
Holmes, J.H. (2000), Learning Classifier Systems Applied to Knowledge Discovery in Clinical Research Databases, in Lanzi, P.L., Stolzmann, W., and Wilson, S.W. (eds.), Learning Classifier Systems: From Foundations to Applications, Springer Lecture Notes in Computer Science 1813, pp. 243–261.
Kovacs, T. (1999) Deletion schemes for classifier systems, in Wolfgang Banzhaf, Jason Daida, Agoston E. Eiben, Max H. Garzon, Vasant Honavar, Mark Jakiela, and Robert E. Smith, editors. Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-99). Morgan Kaufmann, pages 329–336.
Lanzi, P.L. and Riolo, R.L. (2000) A Roadmap to the Last Decase of Learning Classifier System Research (from 1989 to 1999), in Lanzi, P.L., Stolzmann, W., and Wilson, S.W. (eds.), Learning Classifier Systems: From Foundations to Applications, Springer Lecture Notes in Computer Science 1813, pp. 33–61.
Mangasarian, O.L. and Wolberg, W.H. (1990) ‘Cancer diagnosis via linear programming’, SIAM News, 23(5): 1 and 18.
Matheus, C.J. and Rendell, L.A. (1989). Constructive induction on decision trees, in Proceedings of the Eleventh International Joint Conference on Artificial Intelligence, Detroit, MI: Morgan Kaufmann, pp. 117–121.
Mitchell, T. (1997) Machine Learning, McGraw-Hill.
Quinlan, J.R. (1986) Induction of Decision Trees, Machine Learning, 1(1): 81–106.
Quinlan, J.R. (1993) C4.5: Programs for Machine Learning, Morgan Kaufmann.
Rivest, R.L. (1997) Learning Decision Lists, Machine Learning, 1(2): 229–246.
Saxon, S. and Barry, A. (2000) XCS and the Monk’s Problems, in Lanzi, P.L., Stolzmann, W. and Wilson, S.W. (eds.), Learning Classifier Systems: From Foundations to Applications, Springer Lecture Notes in Computer Science 1813, pp. 223–242.
Schlimmer, J.C. (1987). Concept acquisition through representational adjustment. Doctoral dissertation, Department of Information and Computer Science, University of California, Irvine, CA.
Smith, J.W., Everhart, J.E., Dickson, W.C., Knowler, W.C., and Johannes, R.S. (1988). Using the ADAP learning algorithm to forecast the onset of diabetes mellitus, in Proceedings of the Symposium on Computer Applications and Medical Care, IEEE Computer Society Press, pp. 261–265.
Wilson, S.W. (1995) ‘Classifier fitness based on accuracy’, Evolutionary Computation, 3(2): 149–175.
Wilson, S.W. (2000a) ‘Mining Oblique Data with XCS’, Technical Report 2000028, University of Illinois at Urbana-Champaign, MI, USA.
Wilson, S.W. (2000b) ‘Get Real! XCS with Continuous-Valued Inputs’, in Lanzi, P.L., Stolzmann, W. and Wilson, S.W. (eds.), Learning Classifier Systems: From Foundations to Applications, Springer Lecture Notes in Computer Science 1813, pp. 209–219.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2002 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Dixon, P.W., Corne, D.W., Oates, M.J. (2002). A Preliminary Investigation of Modified XCS as a Generic Data Mining Tool. In: Lanzi, P.L., Stolzmann, W., Wilson, S.W. (eds) Advances in Learning Classifier Systems. IWLCS 2001. Lecture Notes in Computer Science(), vol 2321. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-48104-4_9
Download citation
DOI: https://doi.org/10.1007/3-540-48104-4_9
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-43793-2
Online ISBN: 978-3-540-48104-1
eBook Packages: Springer Book Archive