Guided Rule Discovery in XCS for High-Dimensional Classification Problems

  • Mani Abedini
  • Michael Kirley
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7106)


XCS is a learning classifier system that combines a reinforcement learning scheme with evolutionary algorithms to evolve a population of classifiers in the form of condition-action rules. In this paper, we investigate the effectiveness of XCS in high-dimensional classification problems where the number of features greatly exceeds the number of data instances – common characteristics of microarray gene expression classification tasks. We introduce a new guided rule discovery mechanisms for XCS, inspired by feature selection techniques commonly used in machine learning. The extracted feature quality information is used to bias the evolutionary operators. The performance of the proposed model is compared with the standard XCS model and a number of well-known machine learning algorithms using benchmark binary classification tasks and gene expression data sets. Experimental results suggests that the guided rule discovery mechanism is computationally efficient, and promotes the evolution of more accurate solutions. The proposed model performs significantly better than comparative algorithms when tackling high-dimensional classification problems.


Information Gain Evolutionary Operator Feature Selection Technique Uniform Crossover Rule Discovery 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. 1.
    UCI Machine Learning Repository,
  2. 2.
    Alon, U., Barkai, N., Notterman, D.A., Gishdagger, K., Ybarradagger, S., Mackdagger, D., Levine, A.J.: Broad patterns of gene expression revealed by clustering analysis of tumor and normal colon tissues probed by oligonucleotide arrays. Proc. of the National Academy of Sciences of the USA 96, 6745–6750 (1999)CrossRefGoogle Scholar
  3. 3.
    Bacardit, J., Krasnogor, N.: Smart crossover operator with multiple parents for a Pittsburgh learning classifier system. In: Proceedings of the 8th Conference on GECCO, pp. 1441–1448. ACM (2006)Google Scholar
  4. 4.
    Bonilla Huerta, E., Hernández Hernández, J.C., Hernández Montiel, L.A.: A New Combined Filter-Wrapper Framework for Gene Subset Selection with Specialized Genetic Operators. In: Martínez-Trinidad, J.F., Carrasco-Ochoa, J.A., Kittler, J. (eds.) MCPR 2010. LNCS, vol. 6256, pp. 250–259. Springer, Heidelberg (2010), CrossRefGoogle Scholar
  5. 5.
    Butz, M., Pelikan, M., Lloral, X., Goldberg, D.E.: Automated global structure extraction for effective local building block processing in XCS. Evolutionary Computation 14(3), 345–380 (2006)CrossRefGoogle Scholar
  6. 6.
    Butz, M.V., Goldberg, D.E., Tharakunnel, K.: Analysis and improvement of fitness exploitation in XCS: bounding models, tournament selection, and bilateral accuracy. Evol. Comput. 11, 239–277 (2003)CrossRefGoogle Scholar
  7. 7.
    Butz, M.V., Wilson, S.W.: An Algorithmic Description of XCS. In: Lanzi, P.L., Stolzmann, W., Wilson, S.W. (eds.) IWLCS 2000. LNCS (LNAI), vol. 1996, pp. 253–274. Springer, Heidelberg (2001)CrossRefGoogle Scholar
  8. 8.
    Fernandndez, A., Garcianda, S., Luengo, J., Bernado-Mansilla, E., Herrera, F.: Genetics-based machine learning for rule induction: State of the art, taxonomy, and comparative study. IEEE Transactions on Evolutionary Computation 14(6), 913–941 (2010)CrossRefGoogle Scholar
  9. 9.
    Golub, T.R., Slonim, D.K., Tamayo, P., Huard, C., Gaasenbeek, M., Mesirov, J.P., Coller, H., Loh, M.L., Downing, J.R., Caligiuri, M.A., Bloomfield, C.D.: Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. Science 286, 531–537 (1999)CrossRefGoogle Scholar
  10. 10.
    Hedenfalk, I., Duggan, D., Chen, Y., Radmacher, M., Bittner, M., Simon, R., Meltzer, P., Gusterson, B., Esteller, M., Kallioniemi, O.P., Wilfond, B., Borg, A., Trent, J.: Gene-Expression profiles in hereditary breast cancer. N. Engl. J. Med. 344(8), 539–548 (2001)CrossRefGoogle Scholar
  11. 11.
    Isabelle Guyon, M.N., Gunn, S., Zadeh, L. (eds.): Feature Extraction, Foundations and Applications. Springer, Heidelberg (2006)zbMATHGoogle Scholar
  12. 12.
    Jose-Revuelta, L.M.S.: A Hybrid GA-TS Technique with Dynamic Operators and its Application to Channel Equalization and Fiber Tracking. I-Tech Education and Publishing (2008)Google Scholar
  13. 13.
    Lanzi, P.L.: A Study of the Generalization Capabilities of XCS. In: Bäck, T. (ed.) Proceedings of the 7th International Conference on Genetic Algorithms, pp. 418–425. Morgan Kaufmann (1997)Google Scholar
  14. 14.
    Liu, H., Motoda, H.: Computational Methods of Feature Selection. Data Mining and Knowledge Discovery Series. Chapman & Hall/CRC (2007)Google Scholar
  15. 15.
    Moore, J.H., White, B.C.: Exploiting Expert Knowledge in Genetic Programming for Genome-Wide Genetic Analysis. In: Runarsson, T.P., Beyer, H.-G., Burke, E.K., Merelo-Guervós, J.J., Whitley, L.D., Yao, X. (eds.) PPSN 2006. LNCS, vol. 4193, pp. 969–977. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  16. 16.
    Morales-Ortigosa, S., Orriols-Puig, A., Bernadó-Mansilla, E.: New Crossover Operator for Evolutionary Rule Discovery in XCS. In: 8th International Conference on Hybrid Intelligent Systems, pp. 867–872. IEEE Computer Society (2008)Google Scholar
  17. 17.
    Morales-Ortigosa, S., Orriols-Puig, A., Bernadó-Mansilla, E.: Analysis and improvement of the genetic discovery component of XCS. In: International Joint Conference on Hybrid Intelligent Systems, vol. 6, pp. 81–95 (April 2009)Google Scholar
  18. 18.
    Orriols-Puig, A., Casillas, J., Bernadó-Mansilla, E.: Genetic-based machine learning systems are competitive for pattern recognition. Evolutionary Intelligence 1, 209–232 (2065), doi:10.1007/s12065-008-0013-9CrossRefGoogle Scholar
  19. 19.
    Singh, D., Febbo, P.G., Ross, K., Jackson, D.G., Manola, J., Ladd, C., Tamayo, P., Renshaw, A.A.: Gene expression correlates of clinical prostate cancer behavior. Cancer Cell 1, 203–209 (2002)CrossRefGoogle Scholar
  20. 20.
    Wang, P., Weise, T., Chiong, R.: Novel evolutionary algorithms for supervised classification problems: an experimental study. Evolutionary Intelligence 4(1), 3–16 (2011)CrossRefGoogle Scholar
  21. 21.
    Wilson, S.W.: Classifier Fitness Based on Accuracy. Evolutionary Computation 3(2), 149–175 (1995), CrossRefGoogle Scholar
  22. 22.
    Wilson, S.W.: Get Real! XCS with Continuous-Valued Inputs. In: Lanzi, P.L., Stolzmann, W., Wilson, S.W. (eds.) IWLCS 1999. LNCS (LNAI), vol. 1813, pp. 209–222. Springer, Heidelberg (2000)CrossRefGoogle Scholar
  23. 23.
    Wu, F.-X., Zhang, W., Kusalik, A.: On Determination of Minimum Sample Size for Discovery of Temporal Gene Expression Patterns. In: First International Multi-Symposiums on Computer and Computational Sciences, pp. 96–103 (2006)Google Scholar
  24. 24.
    Zhang, Y., Rajapakse, J.C.: Machine Learning in Bioinformatics, 1st edn. Wiley Series in Bioinformatics (2008)Google Scholar

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© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Mani Abedini
    • 1
  • Michael Kirley
    • 1
  1. 1.Department of Computer Science and Software EngineeringThe University of MelbourneAustralia

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