Have a personal or library account? Click to login
Self-Configuring Hybrid Evolutionary Algorithm for Fuzzy Imbalanced Classification with Adaptive Instance Selection Cover

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

Open Access
|Jun 2016

References

  1. [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.2009.2039140
  2. [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)90050-7
  3. [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. [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.2012.2215331
  5. [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.2004.841738
  6. [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_50
  7. [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.2012.6256587
  8. [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_35
  9. [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.2004.09.043
  10. [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_21
  11. [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.2008.11.004
  12. [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.2007.12.023
  13. [13] Asuncion A., Newman D. UCI machine learning repository, University of California, Irvine, School of Information and Computer Sciences, 2007.
  14. [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-y
  15. [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.2009.03.002
  16. [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.2011.2147794
  17. [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/s12293-008-0005-4
Language: English
Page range: 173 - 188
Published on: Jun 10, 2016
Published by: SAN University
In partnership with: Paradigm Publishing Services
Publication frequency: 4 issues per year

© 2016 Vladimir Stanovov, Eugene Semenkin, Olga Semenkina, published by SAN University
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.