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

[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 - 941.10.1109/TEVC.2009.2039140Search in Google Scholar

[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. 235-282.10.1016/0004-3702(89)90050-7Search in Google Scholar

[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.Search in Google Scholar

[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 2013.10.1109/TFUZZ.2012.2215331Search in Google Scholar

[5] Ishibuchi H., T. Yamamoto, Rule weight specification in fuzzy rule-based classification systems, IEEE Trans. Fuzzy Systems 13, 2005, pp. 428-435.10.1109/TFUZZ.2004.841738Search in Google Scholar

[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. 414-421.10.1007/978-3-642-30976-2_50Search in Google Scholar

[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, 2012.10.1109/CEC.2012.6256587Search in Google Scholar

[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. 310-317.10.1007/978-3-319-11857-4_35Search in Google Scholar

[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 953-963.10.1016/j.patrec.2004.09.043Search in Google Scholar

[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 271-284.10.1007/3-540-32400-3_21Search in Google Scholar

[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. 561-577.10.1016/j.ijar.2008.11.004Search in Google Scholar

[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 - 2398.10.1016/j.fss.2007.12.023Search in Google Scholar

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

[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. 2009.10.1007/s00500-008-0323-ySearch in Google Scholar

[15] Sokolova M., Lapalme G. A systematic analysis of performance measures for classification tasks. - Information Processing and Management 45, 2009, pp. 427-437.10.1016/j.ipm.2009.03.002Search in Google Scholar

[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. 857-872.10.1109/TFUZZ.2011.2147794Search in Google Scholar

[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. 55-67.10.1007/s12293-008-0005-4Search in Google Scholar