A genetic-based approach to features selection for ensembles using a hybrid and adaptive fitness function

Proceedings of the International Joint Conference on Neural Networks

Volumn , Issue , 2012, Pages

  Canuto, Anne M P ^a   Nascimento, Diego S C ^b  

^a FEDERAL UNIVERSITY OF RIO GRANDE DO NORTE   (Brazil)

^b Centro Federal de Educacao Tecnologica do Rio Grande do Norte   (Brazil)

Author keywords

[No Author keywords available]

Indexed keywords

ADAPTIVE FITNESS; AUTOMATIC SELECTION; COMBINATION METHOD; DATA SETS; ENSEMBLE SYSTEMS; FEATURE SELECTION METHODS; FEATURES SELECTION; FILTER APPROACH; FITNESS FUNCTIONS; INDIVIDUAL CLASSIFIERS;

DATA PROCESSING; GENETIC ALGORITHMS; HEALTH; LEARNING ALGORITHMS; NEURAL NETWORKS;

FEATURE EXTRACTION;

EID: 84865090987     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/IJCNN.2012.6252740     Document Type: Conference Paper

References (22)

1. I. H. Witten and E. Frank, Data Mining: Pratical Machine Learning Tools and Techiniques, 2nd ed. Elsevier, 2005.
2. L. I. Kuncheva, Combining Pattern Classifiers: Methods and Algorithms. Wiley, 2004.
3. A. L. V. Coelho and D. S. C. Nascimento, "On the evolutionary design of heterogeneous bagging models," Neurocomputing, vol. 73, pp. 3319-3322, 2010.
4. M. Bacauskiene, A. Verikas, A. Gelzinis, and D. Valincius, "A feature selection technique for generation of classification committees and its application to categorization of laryngeal images," Pattern Recogn, vol. 42, pp. 645-654, May 2009.
5. J. Derrac, S. García, and F. Herrera, "A first study on the use of coevolutionary algorithms for instance and feature selection," in Hybrid Artificial Intelligence Systems, ser. Lecture Notes in Computer Science, E. Corchado, X. Wu, E. Oja, l. Herrero, and B. Baruque, Eds. Springer Berlin / Heidelberg, 2009, vol. 5572, pp. 557-564.
6. H. R. Kanan and K. Faez, "An improved feature selection method based on ant colony optimization (aco) evaluated on face recognition system," Applied Mathematics and Computation, vol. 205, no. 2, pp. 716-725, 2008, special Issue on Advanced Intelligent Computing Theory and Methodology in Applied Mathematics and Computation.
7. D. W. Opitz, "Feature selection for ensembles," in In Proceedings of 16th National Conference on Artificial Intelligence. Press, 1999, pp. 379-384.
8. E. Tuv, A. Borisov, G. Runger, K. Torkkola, I. Guyon, and A. R. Saffari, "Feature selection with ensembles, artificial variables, and redundancy elimination," JMLR, 2009.
9. L. E. A. Santana, L. Silva, A. M. P. Canuto, F. Pintro, and K. O. Vale, "A comparative analysis of genetic algorithm and ant colony optimization to select attributes for an heterogeneous ensemble of classifiers," in IEEE Congress on Evolutionary Computation, 2010, pp. 1-8.
10. L. Breiman, "Bagging predictors," Machine Learning, vol. 24, no. 2, pp. 123-140, 1996.
11. T. G. Dietterich, "An experimental comparison of three methods for constructing ensembles of decision trees: Bagging, boosting and randomization," Machine Learning, vol. 40, no. 2, pp. 139-158, 2000.
12. L. I. Kuncheva and C. J. Whitaker, "Measures of diversity in classifier ensembles," Machine Learning, vol. 51, pp. 181-207, 2003.
13. A. L. V. Coelho, "Evolução, simbiose e hibridismo aplicados à engenharia de sistemas inteligentes modulares: Investigação em redes neurais artificiais, comitês de máquinas e sistemas multiagentes," Ph.D. dissertation, Faculdade de Engenharia Elétrica e Computação, Universidade Estadual de Campinas, 2004.
14. G. Brown and L. Kuncheva, ""good" and "bad" diversity in majority vote ensembles," in Multiple Classifier Systems, ser. Lecture Notes in Computer Science, N. El Gayar, J. Kittler, and F. Roli, Eds. Springer Berlin / Heidelberg, 2010, vol. 5997, pp. 124-133.
15. J. H. Holland, Adaptation in Natural and Artificial Systems, 1975.
16. -, Adaptation in natural and artificial systems. Cambridge, MA, USA: MIT Press, 1992.
17. J. Hua, W. D. Tembe, and E. R. Dougherty, "Performance of featureselection methods in the classification of high-dimension data," Pattern Recogn., vol. 42, pp. 409-424, March 2009.
18. P. Chen and P. Popovich, Correlation: Parametric and Nonparametric Measures, 1st ed. Sage Publications, 2002.
19. A. Asunción and D. J. Newman, UCI Machine Learning Repository, University of California at Irvine, http://ics.uci.edu/~mlearn/MLRepository. html, 2007.
20. D. S. C. Nascimento and A. L. V. Coelho, "Ensembling heterogeneous learning models with boosting," in Procs. of 16th. International Conference on Neural Information Processing (ICONIP), ser. Lecture Notes in Computer Science, C. S. Leung, M. Lee, and J. H. Chan, Eds., vol. 5863, 2009, pp. 512-519.
21. A. M. P. Canuto, M. C. C. Abreu, L. de M. Oliveira, J. C. X. Jr., and A. de M. Santos, "Investigating the influence of the choice of the ensemble members in accuracy and diversity of selection-based and fusion-based methods for ensembles," Pattern Recognition Letters, vol. 28, no. 4, pp. 472-486, 2007.
22. D. Opitz and R. Maclin, "Popular ensemble methods: An empirical study," Journal of Artificial Intelligence Research, vol. 11, pp. 169-198, 1999.