Filter-based optimization techniques for selection of feature subsets in ensemble systems

Expert Systems with Applications

Volumn 41, Issue 4 PART 2, 2014, Pages 1622-1631

  Dos S Santana, Laura Emmanuella A ^a   De Paula Canuto, Anne M ^a  

^a Federal University of RN   (Brazil)

Author keywords

Ant colony optimization; Ensemble systems; Feature selection; Genetic algorithms; Particle swarm optimization

Indexed keywords

EMPIRICAL ANALYSIS; ENSEMBLE SYSTEMS; EVALUATION CRITERIA; FEATURE SELECTION METHODS; INDIVIDUAL CLASSIFIERS; INDIVIDUAL COMPONENTS; OPTIMIZATION TECHNIQUES; SELECTION TECHNIQUES;

ANT COLONY OPTIMIZATION; GENETIC ALGORITHMS; PARTICLE SWARM OPTIMIZATION (PSO); SET THEORY;

FEATURE EXTRACTION;

EID: 84888352153     PISSN: 09574174     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.eswa.2013.08.059     Document Type: Article

References (37)

1. R.E. Banfield, L.O. Hall, K.W. Bowyer, and W.P. Kegelmeyer Ensemble diversity measures and their application to thinning Information Fusion 2005 49 62
2. A.M.P. Canuto, M.C.C. Abreu, L.M. Oliveira, J.C. Xavier-Jr, and A.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 28 4 2007 472 486 (Pubitemid 44918135)
3. A.M.P. Canuto, K.M.O. Vale, and A. Feitosa-Neto Reinsel: A class-based mechanism for feature selection in ensemble of classifiers Applied Soft Computing 12 8 2012 2517 2529
4. P.Y. Chen, and P.M. Popovich Correlation: Parametric and non-parametric measures 2002 Sage Publications
5. E.S. Correa, A.A. Freitas, and C.G. Johnson Particle swarm for attribute selectionin Bayesian classification: An application to protein function prediction Journal of Artificial Evolution and Applications 2008 2008 1 12
6. K. Deb, A. Pratap, S. Agarwal, and T. Meyarivan A fast elitist multi-objective genetic algorithm: NSGA-II IEEE Transactions on Evolutionary Computation 6 2000 182 197
7. J. Demšar Statistical comparisons of classifiers over multiple data sets The Journal of Machine Learning Research 7 2006 1 30 (Pubitemid 43022939)
8. Dorigo, M. (1992). Optimization, learning and natural algorithms [in italian]. Ph.D. thesis, Dipartimento di Elettronica, Politecnico di Milano, Italy.
9. Durillo, J., Nebro, A., Luna, F., Dorronsoro, B., & Alba, E. (2006). jmetal: A java framework for developing multi-objective optimization metaheuristics. Technical report, Department of Languages and Computer Sciences.
10. A. Frank, and A. Asuncion UCI machine learning repository 2010
11. FVC2004. .
12. Garofolo, J. S., Lamel, L. F., Fisher, W. M., Fiscus, J. G., Pallett, D. S., Dahlgren, N. L., et al. (1993). TIMIT Acoustic-Phonetic Continuous Speech Corpus. Philadelphia: Linguistic Data Consortium.
13. Holland, J. H. (1975). Adaptation in natural and artificial systems.
14. J.H. Holland Adaptation in natural and artificial systems: An introductory analysis with applications to biology Control and artificial intelligence 1992 MIT Press, Cambridge MA, USA
15. C. Huang, and C. Wang A GA-based feature selection and parameters optimization for support vector machines Expert Systems with Applications 31 2 2006 231 240 (Pubitemid 44294444)
16. J. Hua, W.D. Tembe, and E.R. Dougherty Performance of feature-selection methods in the classification of high-dimension data Pattern Recognition 42 3 2009 409 424
17. I. Inza, P. Larraaga, R. Etxeberria, and B. Sierra Feature subset selection by Bayesian network-based optimization Artificial Intelligence 123 1-2 2000 157 184
18. S. Iredi, D. Merkle, and M. Middendorf Bi-criterion optimization with multi colony ant algorithms E. Zitzler, L. Thiele, K. Deb, C. Coello Coello, D. Corne, Evolutionary multi-criterion optimization LNCS Vol. 19 2001 Springer Berlin, Heidelberg 359 372 (Pubitemid 33241399)
19. Kennedy, J., & Eberhart, R. C. (1995). Particle swarm optimization. In IEEE international conference on neural networks (pp. 1942-1948).
20. N.Y. Khan, M.Y. Javed, N. Khattak, and U.M.Y. Chang Optimization of core point detection in fingerprints Proceedings of the 9th biennial conference of the Australian pattern recognition society on digital image computing techniques and applications, DICTA '07 2007 IEEE Computer Society Washington, DC, USA 260 266
21. T. Kinnunen, and H. Li An overview of text-independent speaker recognition: From features to supervectors Speech Communication 52 1 2010 12 40
22. J. Kittler Feature set search algorithms Pattern Recognition and Signal Processing 1978 41 60
23. Ko, A. -R., Sabourin, R., de Souza Britt, A. (2006). Combining diversity and classification accuracy for ensemble selection in random subspaces. In International joint conference on neural networks (IJCNN) (pp. 2144 -2151). (Pubitemid 351369591)
24. R. Kohavi, and G.H. John Wrappers for feature subset selection Artificial Intelligence 97 1-2 1997 273 324 (Pubitemid 127401107)
25. L.I. Kuncheva Combining pattern classifiers: Methods and algorithms 2004 Wiley-Interscience
26. Lee, M., Boroczky, L., Sungur-Stasik, K., Cann, A., Borczuk, A., Kawut, S., & Powell, C. (2008). A two-step approach for feature selection and classifier ensemble construction in computer-aided diagnosis. In IEEE international symposium on computer-based medical systems (CBMS) (pp. 548 -553).
27. S. Mao, L. Jiao, L. Xiong, and S. Gou Greedy optimization classifiers ensemble based on diversity Pattern Recognition 44 6 2011 1245 1261
28. Nascimento, D., & Canuto, A. M. P. (2012). A genetic-based approach to features selection for ensembles using a hybrid and adaptive fitness function. In IEEE international joint conference on neural networks (IJCNN) (pp. 2694-2701).
29. L.S. Oliveira, M. Morita, and R. Sabourin Feature selection for ensembles applied to handwriting recognition International Journal on Document Analysis and Recognition 8 4 2006 262 279 (Pubitemid 44251801)
30. D.W. Opitz Feature selection for ensembles Conference on artificial intelligence, AAAI 1999 American Association for Artificial Intelligence 379 384
31. K.R. Robbins, W. Zhang, J.K. Bertrand, and R. Rekaya The ant colony algorithm for feature selection in high-dimension gene expression data for disease classification Journal of Mathematical Medicine and Biology 24 4 2007 413 426
32. Santana, L. E. (2012). Optimization in ensemble systems: A filter-based approach to select subsets of attributes [in portuguese]. Ph.D. thesis, Federal University of Rio Grande do Norte, Brazil.
33. Santana, L. E. A., Silva, L., Canuto, A. M. P., Pintro, F., & Vale, K. O. (2010). 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 (pp. 1-8).
34. Santana, L., Canuto, A., & Silva, L. (2011). Bio-inspired meta-heuristic as feature selector in ensemble systems: A comparative analysis. In International joint conference on neural networks (IJCNN) (pp. 1112 -1119).
35. W. Siedlecki, and J. Sklansky A note on genetic algorithms for large-scale feature selection Pattern Recognition Letters 10 5 1989 335 347
36. Yu, S. (2003). Feature selection and classifier ensembles: A study on hyperspectral remote sensing data. Ph.D. thesis, University of Antwerp, Antwerp.
37. Zitzler, E., Laumanns, M., & Thiele, L. (2001). Spea2: Improving the strength pareto evolutionary algorithm. Tech. rep., Department of Electrical Engineering, Swiss Federal Institute of Technology.