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
Baluja, S.: Population-based incremental learning: A method for integrating genetic search based function optimization and competitive learning. Technical report, Pittsburgh, PA, USA (1994)
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)
Cover, T.M., Hart, P.E.: Nearest neighbor pattern classification. IEEE Transactions on Information Theory 13, 21–27 (1967)
Demšar, J.: Statistical comparisons of classifiers over multiple data sets. Journal of Machine Learning Research 7, 1–30 (2006)
Eiben, A.E., Smith, J.E.: Introduction to Evolutionary Computing. Springer, Heidelberg (2003)
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)
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)
Freitas, A.A.: Data Mining and Knowledge Discovery with Evolutionary Algorithms. Springer, New York (2002)
Ghosh, A., Jain, L.C.: Evolutionary Computation in Data Mining. Springer, Berlin (2005)
Goldberg, D.E.: Genetic Algorithms in Search, Optimization, and Machine Learning. Addison-Wesley, Reading (1989)
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)
Liu, H., Motoda, H.: Instance Selection and Construction for Data Mining. The Springer International Series in Engineering and Computer Science (2001)
Liu, H., Motoda, H.: Computational Methods of Feature Selection. Chapman & Hall/Crc Data Mining and Knowledge Discovery Series (2007)
Oh, I., Lee, J., Moon, B.: Hybrid Genetic Algorithms for Feature Selection. IEEE Transactions on Pattern Analysis and Machine Intelligence 26, 1424–1437 (2004)
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)
Panait, L., Luke, S., Harrison, J.F.: Archive-Based Cooperative Cooevolutionary Algorithms. In: Genetic and Evolutionary Computation Conference, pp. 345–352 (2006)
Potter, M.A., De Jong, K.A.: Cooperative coevolution: an architecture for evolving coadapted subcomponents. Evolutionary Computation 8, 1–29 (2000)
Pyle, D.: Data Preparation for Data Mining. The Morgan Kaufmann Series in DMS (1999)
Newman, D.J., Hettich, S., Merz, C.B.: UCI repository of ML databases (1998)
Whitley, D.: The GENITOR Algorithm and selective preasure: Why Rank Based Allocation of Reproductive Trials is Best. Genetic Algorithms, 116–121 (1989)
Jansen, T., Wiegand, R.P.: The Cooperative Coevolutionary (1+1) EA. Evolutionary Computation 12, 405–434 (2004)
Wolpert, D., Macready, W.: Coevolutionary Free Lunches. IEEE Transactions on Evolutionary Computation 9, 721–735 (2005)
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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
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