Self-Configuring Hybrid Evolutionary Algorithm for Fuzzy Imbalanced Classification with Adaptive Instance Selection

Vladimir Stanovov 1 , Eugene Semenkin 1  and Olga Semenkina 1
  • 1 Institute of Computer Science and Telecommunications, Siberian State Aerospace University Krasnoyarskii rabochii ave. 31, 660014, Krasnoyarsk, Russian Federation


A novel approach for instance selection in classification problems is presented. This adaptive instance selection is designed to simultaneously decrease the amount of computation resources required and increase the classification quality achieved. The approach generates new training samples during the evolutionary process and changes the training set for the algorithm. The instance selection is guided by means of changing probabilities, so that the algorithm concentrates on problematic examples which are difficult to classify. The hybrid fuzzy classification algorithm with a self-configuration procedure is used as a problem solver. The classification quality is tested upon 9 problem data sets from the KEEL repository. A special balancing strategy is used in the instance selection approach to improve the classification quality on imbalanced datasets. The results prove the usefulness of the proposed approach as compared with other classification methods.

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  • [1] A. Fernandez, S. Garcia, J. Luengo, E. Bernado- Mansilla, F. Herrera, Genetics-Based Machine Learning for Rule Induction: State of the Art, Taxonomy, and Comparative Study, Evolutionary Computation, IEEE Transactions on (Volume:14, Issue: 6), June 21, 2010, pp. 913 - 941.

  • [2] L. B. Booker, D. E. Goldberg, and J. H. Holland, Classifier systems and genetic algorithms, Artif. Intell., vol. 40, no. 1-3, Sep. 1989, pp. 235-282.

  • [3] Bodenhofer U., Herrera F. Ten Lectures on Genetic Fuzzy Systems, Preprints of the International Summer School: Advanced Control- Fuzzy, Neural, Genetic. - Slovak Technical University, Bratislava., 1997. p. 1-69.

  • [4] Ishibuchi H., Mihara S., Nojima Y. Parallel Distributed Hybrid Fuzzy GBML Models With Rule Set Migration and Training Data Rotation, IEEE Transactions on fuzzy systems, vol. 21, n. 2., April 2013.

  • [5] Ishibuchi H., T. Yamamoto, Rule weight specification in fuzzy rule-based classification systems, IEEE Trans. Fuzzy Systems 13, 2005, pp. 428-435.

  • [6] E. Semenkin, M. Semenkina, Self-configuring genetic algorithm with modified uniform crossover operator, in Y. Tan, Y. Shi, Z. Ji (Eds.), Advances in Swarm Intelligence, PT1, LNCS 7331, 2012, pp. 414-421.

  • [7] E. Semenkin, M. Semenkina, Self-Configuring Genetic Programming Algorithm with Modified Uniform Crossover, in Proc. of the IEEE Congress on Evolutionary Computation (CEC 2012), Brisane (Australia), pp. 1-6, 2012.

  • [8] M. Semenkina, E. Semenkin, Hybrid selfconfiguring evolutionary algorithm for automated design of fuzzy classifier, in Y. Tan, Y. Shi, C.A.C. Coello (Eds.), Advances in Swarm Intelligence, PT1, LNCS 8794, 2014, pp. 310-317.

  • [9] J. R. Cano, F. Herrera, M. Lozano, Stratification for scaling up evolutionary prototype selection, Pattern Recognition Letters, 2004 Volume 26, Issue 7, 15 May 2005, Pages 953-963.

  • [10] J. R. Cano, F. Herrera, M. Lozano, A Study on the Combination of Evolutionary Algorithms and Stratified Strategies for Training Set Selection in Data Mining, Advances in Soft Computing Volume 32, 2005, pp 271-284.

  • [11] A. Fernndez, M. J. Jesus, F. Herrera, Hierarchical fuzzy rule based classification systems with genetic rule selection for imbalanced data-sets, International Journal of Approximate Reasoning, 2009, pp. 561-577.

  • [12] A. Fernndez, S. Garca, M. J. Jesusb, F. Herrera, A study of the behaviour of linguistic fuzzy rule based classification systems in the framework of imbalanced data-sets, Fuzzy Sets and Systems 159, 2008, pp. 2378 - 2398.

  • [13] Asuncion A., Newman D. UCI machine learning repository, University of California, Irvine, School of Information and Computer Sciences, 2007.

  • [14] J. Alcal-Fdez, L. Snchez, S. Garcia, M. J. del Jesus, S. Ventura, J. M. Garrell, J. Otero, C. Romero, J. Bacardit, V. M. Rivas, J. C. Fernndez, and F. Herrera, KEEL: A software tool to assess evolutionary algorithms for data mining problems, Soft Comput., vol. 13, no. 3, pp. 307-318, Feb. 2009.

  • [15] Sokolova M., Lapalme G. A systematic analysis of performance measures for classification tasks. - Information Processing and Management 45, 2009, pp. 427-437.

  • [16] J. Alcala-Fdez, R. Alcala, F. Herrera, A fuzzy association rulebased classification model for highdimensional problems with genetic rule selection and lateral tuning, IEEE Trans. Fuzzy Syst., vol. 19, no. 5, Oct. 2011, pp. 857-872.

  • [17] J. Bacardit, E. K. Burke, N. Krasnogor, Improving the scalability of rule-based evolutionary learning, Memetic Comput. J., vol. 1, no. 1, Mar. 2009, pp. 55-67.


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