A GA-based feature selection approach with an application to handwritten character recognition

Pattern Recognition Letters

Volumn 35, Issue 1, 2014, Pages 130-141

  De Stefano, C ^a   Fontanella, F ^a   Marrocco, C ^a   Scotto Di Freca, A ^a  

^a Ass EURATOM ENEA CREATE DIEI Univ di Cassino e del Lazio Meridionale   (Italy)

Author keywords

Feature selection; Genetic algorithms; Handwriting recognition

Indexed keywords

CLASSIFICATION METHODS; CLASSIFICATION SCHEME; COVARIANCE MATRICES; FEATURE SELECTION ALGORITHM; FISHER LINEAR DISCRIMINANTS; HAND WRITTEN CHARACTER RECOGNITION; HANDWRITING RECOGNITION; STATISTICAL PROPERTIES;

CHARACTER RECOGNITION; COVARIANCE MATRIX; FEATURE EXTRACTION; PROBABILITY DISTRIBUTIONS;

GENETIC ALGORITHMS;

EID: 84887472714     PISSN: 01678655     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.patrec.2013.01.026     Document Type: Article

References (43)

1. R. Ahlgren, H. Ryan, and C. Swonger A character recognition application of an iterative procedure for feature selection IEEE Trans. Comput. C-20 9 1971 1067 1075
2. M. Ben-Bassat Pattern recognition and reduction of dimensionality P. Krishnaiah, L. Kanal, Handbook of Statistics-II 1982 North Holland 1982 773 791
3. T. Blickle, and L. Thiele A comparison of selection schemes used in evolutionary algorithms Evol. Comput. 4 4 1996 361 394 (Pubitemid 126706163)
4. Breukelen, M.V.; Duin, R.; Tax, D.; den Hartog, J.; 1997. Combining classifiers for the recognition of handwritten digits. In: 1st internat. workshop on statistical techniques in, pattern recognition, pp. 13-18.
5. Chang, C.-C.; Lin, C.-J.; 2001. LIBSVM: A library for support vector machines, available at .
6. H. Chouaib, O.R. Terrades, S. Tabbone, F. Cloppet, and N. Vincent Feature selection combining genetic algorithm and adaboost classifiers Proc. of Internat. Conf. on Pattern Recognition (ICPR 2008) 2008 IEEE
7. K. Chung, and J. Yoon Performance comparison of several feature selection methods based on node pruning in handwritten character recognition Proc. 4th Internat. Conf. on Document Analysis and Recognition (ICDAR 97) 1997 IEEE Computer Society 11 15
8. Cordella, L.; De Stefano, C.; Fontanella, F.; Marrocco, C.; 2008. A feature selection algorithm for handwritten character recognition. In: 19th Internat. Conf. on Pattern Recognition (ICPR 2008), 2008, pp. 1-4.
9. Cordella, L.; De Stefano, C.; Fontanella, F.; Marrocco, C.; Scotto di Freca, A.; 2010. Combining single class features for improving performance of a two stage classifier. In: 20th Internat. Conf. on Pattern Recognition (ICPR 2010), pp. 4352-4355.
10. M. Dash, and H. Liu Consistency-based search in feature selection Artif. Intell. 151 1-2 2003 155 176
11. De Stefano, C.; Fontanella, F.; Marrocco, C.; Schirinzi, G.; 2007. A feature selection algorithm for class discrimination improvement. In: Geoscience and Remote Sensing, Symposium, 2007 (IGARSS07), pp. 425-428.
12. Fodor, I.; 2002. A survey of dimension reduction techniques, Tech. rep.; Center for Applied Scientinc Computing, Lawrence Livermore National Laboratory.
13. Frank, A.; Asuncion, A.; 2010. UCI machine learning repository. URL
14. K. Fukunaga Introduction to Statistical Pattern Recognition second ed. 1990 Academic Press Professional, Inc. San Diego, CA, USA
15. D.E. Goldberg Genetic Algorithms in Search Optimization and Machine Learning 1989 Addison-Wesley
16. Grother, P.J.; 1995. Nist special database 19. URL
17. M. Gütlein, E. Frank, M. Hall, and A. Karwath Large-scale attribute selection using wrappers Proc. IEEE Symposium on Computational Intelligence and Data Mining 2009 IEEE 332 339
18. I. Guyon, and A. Elisseeff An introduction to variable and feature selection J. Machine Learn. Res. 3 2003 1157 1182
19. M.A. Hall Correlation-based feature selection for discrete and numeric class machine learning ICML '00: Proc. Seventeenth Internat. Conf. on Machine Learning 2000 Morgan Kaufmann Publishers Inc. San Francisco, CA, USA 359 366
20. Hall, M.A.; 1998. Correlation-based feature subset selection for machine learning, Ph.D. thesis, University of Waikato, Hamilton, New Zealand.
21. M. Hall, E. Frank, G. Holmes, B. Pfahringer, P. Reutemann, and I.H. Witten The weka data mining software: An update SIGKDD Explor. 11 1 2009 10 18
22. P.E. Hart, R.O. Duda, and D.G. Stork Pattern Classification 2001 John Wiley & sons Inc.
23. T.K. Ho, and M. Basu Complexity measures of supervised classification problems IEEE Trans. Pattern Anal. Machine Intell. 24 3 2002 289 300 (Pubitemid 35289347)
24. G. Kim, and V. Govindaraju A lexicon driven approach to handwritten word recognition for real-time applications IEEE Trans. Pattern Anal. Machine Intell. 19 4 1997 366 379 (Pubitemid 127762892)
25. G. Kim, and S. Kim Feature selection using genetic algorithms for handwritten character recognition Proc. 7th Internat. Workshop on Frontiers in Handwriting Recognition (IWFHR 2000) 2000 Amsterdam 2000 103 112
26. M. Kudo, and J. Sklansky Comparison of algorithms that select features for pattern recognition Pattern Recognition 33 1 2000 25 41 (Pubitemid 32081876)
27. N. Kwak, and C.-H. Choi Input feature selection for classification problems IEEE Trans. Neural Networks 13 1 2002 143 159 (Pubitemid 34236844)
28. LeCun, Y.; Cortes, C.; 2010. MNIST handwritten digit database. URL
29. Liu, H.; Setiono, R.; 1996. A probabilistic approach to feature selection - a filter solution. In: 13th Internat. Conf. on Machine Learning, pp. 319-327.
30. R. Meiri, and J. Zahavi Using simulated annealing to optimize the feature selection problem in marketing applications Eur. J. Oper. Res. 171 3 2006 842 858 (Pubitemid 43132950)
31. C.M. Nunes, A.d.S. Britto Jr.; C.A.A. Kaestner, and R. Sabourin An optimized hill climbing algorithm for feature subset selection: Evaluation on handwritten character recognition Proc. 9th Internat. Workshop on Frontiers in Handwriting Recognition (IWFHR'04) 2004 IEEE Computer Society Washington, DC, USA 365 370 (Pubitemid 40601249)
32. I.-S. Oh, J.-S. Lee, and B.-R. Moon Hybrid genetic algorithms for feature selection IEEE Trans. Pattern Anal. Machine Intell. 26 11 2004 1424 1437 (Pubitemid 40168020)
33. L.L.S. Oliveira, R. Sabourin, F. Bortolozzi, and C. Suen Automatic recognition of handwritten numerical strings: A recognition and verification strategy IEEE Trans. Pattern Anal. Machine Intell. 24 11 2002 1438 1454 (Pubitemid 35426860)
34. L.S. Oliveira, R. Sabourin, F. Bortolozzi, and C. Suen A methodology for feature selection using multi-objective genetic algorithms for handwritten digit string recognition Internat. J. Pattern Recognit. Artif. Intell. 17 2003 2003
35. J. Pearl Heuristics: Intelligent Search Strategies for Computer Problem Solving 1984 Addison-Wesley Reading, MA
36. P. Pudil, J. Novovičová, and J. Kittler Floating search methods in feature selection Pattern Recognition Lett. 15 11 1994 1119 1125
37. Shi, D.; Shu, W.; Liu, H.; 1998. Feature selection for handwritten chinese character recognition based on genetic algorithms. In: 1998 IEEE Internat. Conf. on Systems, Man, and Cybernetics, vol. 5, pp. 4201-4206.
38. W. Siedlecki, and J. Sklansky A note on genetic algorithms for large-scale feature selection Pattern Recognition Lett. 10 5 1989 335 347
39. P. Somol, P. Pudil, and J. Kittler Fast branch and bound algorithms for optimal feature selection IEEE Trans. Pattern Anal. Machine Intell. 26 7 2004 900 912
40. V.N. Vapnik Statistical Learning Theory 1998 Wiley-Interscience
41. L. Xu, P. Yan, and T. Chang Best first strategy for feature selection 9th Internat. Conf. on Pattern Recognition 1988 IEEE 706 708
42. J. Yang, and V. Honavar Feature subset selection using a genetic algorithm IEEE Intell. Systems 13 1998 44 49
43. B. Yu, and B. Yuan A more efficient branch and bound algorithm for feature selection Pattern Recognition 26 6 1993 883 889 (Pubitemid 23688578)