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A First Study on the Use of Coevolutionary Algorithms for Instance and Feature Selection

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Hybrid Artificial Intelligence Systems (HAIS 2009)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 5572))

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Abstract

Cooperative Coevolution is a technique in the area of Evolutionary Computation. It has been applied to many combinatorial problems with great success. This contribution proposes a Cooperative Coevolution model for simultaneous performing some data reduction processes in classification with nearest neighbours methods through feature and instance selection.

In order to check its performance, we have compared the proposal with other evolutionary approaches for performing data reduction. Results have been analyzed and contrasted by using non-parametric statistical tests, finally showing that the proposed model outperforms the non-cooperative evolutionary techniques.

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References

  1. Baluja, S.: Population-based incremental learning: A method for integrating genetic search based function optimization and competitive learning. Technical report, Pittsburgh, PA, USA (1994)

    Google Scholar 

  2. Cano, J.R., Herrera, F., Lozano, M.: Using evolutionary algorithms as instance selection for data reduction in KDD: An experimental study. IEEE Transactions on Evolutionary Computation 7, 561–575 (2003)

    Article  Google Scholar 

  3. Cover, T.M., Hart, P.E.: Nearest neighbor pattern classification. IEEE Transactions on Information Theory 13, 21–27 (1967)

    Article  MATH  Google Scholar 

  4. Demšar, J.: Statistical comparisons of classifiers over multiple data sets. Journal of Machine Learning Research 7, 1–30 (2006)

    MathSciNet  MATH  Google Scholar 

  5. Eiben, A.E., Smith, J.E.: Introduction to Evolutionary Computing. Springer, Heidelberg (2003)

    Book  MATH  Google Scholar 

  6. Eshelman, L.J.: The CHC adaptative search algorithm: How to safe search when engaging in nontraditional genetic recombination. In: Foundations of Genetic Algorithms, pp. 265–283 (1990)

    Google Scholar 

  7. Fragoudis, D., Meretakis, D., Likothanassis, S.: Integrating feature and instance selection for text classification. In: 8th ACM SIGKDD international conference on KDD, pp. 501–506 (2002)

    Google Scholar 

  8. Freitas, A.A.: Data Mining and Knowledge Discovery with Evolutionary Algorithms. Springer, New York (2002)

    Book  MATH  Google Scholar 

  9. Ghosh, A., Jain, L.C.: Evolutionary Computation in Data Mining. Springer, Berlin (2005)

    Book  MATH  Google Scholar 

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

    MATH  Google Scholar 

  11. Au, C., Leung, H.: Guided Mutations in Cooperative Coevolutionary Algorithms for Function Optimization. In: IEEE International Conference on Tools with Artificial Intelligence, pp. 407–414 (2007)

    Google Scholar 

  12. Liu, H., Motoda, H.: Instance Selection and Construction for Data Mining. The Springer International Series in Engineering and Computer Science (2001)

    Google Scholar 

  13. Liu, H., Motoda, H.: Computational Methods of Feature Selection. Chapman & Hall/Crc Data Mining and Knowledge Discovery Series (2007)

    Google Scholar 

  14. Oh, I., Lee, J., Moon, B.: Hybrid Genetic Algorithms for Feature Selection. IEEE Transactions on Pattern Analysis and Machine Intelligence 26, 1424–1437 (2004)

    Article  Google Scholar 

  15. Panait, L., Wiegand, R.P., Luke, S.: Improving coevolutionary search for optimal multiagent behaviors. In: International Joint Conferences on Artificial Intelligence, pp. 653–658 (2003)

    Google Scholar 

  16. Panait, L., Luke, S., Harrison, J.F.: Archive-Based Cooperative Cooevolutionary Algorithms. In: Genetic and Evolutionary Computation Conference, pp. 345–352 (2006)

    Google Scholar 

  17. Potter, M.A., De Jong, K.A.: Cooperative coevolution: an architecture for evolving coadapted subcomponents. Evolutionary Computation 8, 1–29 (2000)

    Article  Google Scholar 

  18. Pyle, D.: Data Preparation for Data Mining. The Morgan Kaufmann Series in DMS (1999)

    Google Scholar 

  19. Newman, D.J., Hettich, S., Merz, C.B.: UCI repository of ML databases (1998)

    Google Scholar 

  20. Whitley, D.: The GENITOR Algorithm and selective preasure: Why Rank Based Allocation of Reproductive Trials is Best. Genetic Algorithms, 116–121 (1989)

    Google Scholar 

  21. Jansen, T., Wiegand, R.P.: The Cooperative Coevolutionary (1+1) EA. Evolutionary Computation 12, 405–434 (2004)

    Article  Google Scholar 

  22. Wolpert, D., Macready, W.: Coevolutionary Free Lunches. IEEE Transactions on Evolutionary Computation 9, 721–735 (2005)

    Article  Google Scholar 

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© 2009 Springer-Verlag Berlin Heidelberg

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Derrac, J., García, S., Herrera, F. (2009). A First Study on the Use of Coevolutionary Algorithms for Instance and Feature Selection. In: Corchado, E., Wu, X., Oja, E., Herrero, Á., Baruque, B. (eds) Hybrid Artificial Intelligence Systems. HAIS 2009. Lecture Notes in Computer Science(), vol 5572. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02319-4_67

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  • DOI: https://doi.org/10.1007/978-3-642-02319-4_67

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-02318-7

  • Online ISBN: 978-3-642-02319-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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