Abstract:
Recognition of handwritten digits by computers is a common research topic in the pattern recognition area and has application in several domains. Many techniques can be applied in order to maximize the recognition performance, such as image preprocessing, feature extraction, feature selection and classification stages. This paper focuses on the assessment of three swarm intelligence optimization algorithms for feature selection optimization, called Binary Fish School Search (BFSS), Advanced Binary Ant Colony Optimization (ABACO) and Binary Particle Swarm Optimization (BPSO), for the recognition of handwritten digits. These meta-heuristics were applied to the well-known handwritten digit database MNIST, preprocessed with the CDF 9/7 Wavelet Transform. We used support vector machine (SVM) for the classification task. A considerable reduction in the number of features used for digit classification on the MNIST database with a small loss in the classification rates was observed. © 2015 IEEE.
Registro:
| Documento: |
Conferencia
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| Título: | Metaheuristics for feature selection in handwritten digit recognition |
| Autor: | Seijas, L.M.; Carneiro, R.F.; Santana, C.J., Jr.; Soares, L.S.L.; Bezerra, S.G.T.A.; Bastos-Filho, C.J.A.; Vellasco M.M.B.R.; Tupac Valdivia Y.J.; Lopes H.S. |
| Filiación: | Escola Politécnica de Pernambuco, Universidade de Pernambuco (UPE), Recife, Brazil
Departamento de Computación, Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires, Argentina
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| Palabras clave: | advanced binary ant colony optimization; binary fish school search; binary optimization; binary particle swarm optimization; feature selection; handwritten digit recognition; support vector machine; wavelet transform; Algorithms; Ant colony optimization; Artificial intelligence; Bins; Character recognition; Feature extraction; Fish; Optimization; Particle swarm optimization (PSO); Pattern recognition; Support vector machines; Wavelet transforms; Binary ant colony optimizations; Binary optimization; Binary particle swarm optimization; Fish school searches; Handwritten digit recognition; Classification (of information) |
| Año: | 2016
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| DOI: |
http://dx.doi.org/10.1109/LA-CCI.2015.7435975 |
| Título revista: | 2nd Latin-America Congress on Computational Intelligence, LA-CCI 2015
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| Título revista abreviado: | Latin-Am. Congr. Comput. Intell., LA-CCI
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| Registro: | https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_97814673_v_n_p_Seijas |
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Citas:
---------- APA ----------
Seijas, L.M., Carneiro, R.F., Santana, C.J., Jr., Soares, L.S.L., Bezerra, S.G.T.A., Bastos-Filho, C.J.A., Vellasco M.M.B.R.,..., Lopes H.S.
(2016)
. Metaheuristics for feature selection in handwritten digit recognition. 2nd Latin-America Congress on Computational Intelligence, LA-CCI 2015.
http://dx.doi.org/10.1109/LA-CCI.2015.7435975---------- CHICAGO ----------
Seijas, L.M., Carneiro, R.F., Santana, C.J., Jr., Soares, L.S.L., Bezerra, S.G.T.A., Bastos-Filho, C.J.A., et al.
"Metaheuristics for feature selection in handwritten digit recognition"
. 2nd Latin-America Congress on Computational Intelligence, LA-CCI 2015
(2016).
http://dx.doi.org/10.1109/LA-CCI.2015.7435975---------- MLA ----------
Seijas, L.M., Carneiro, R.F., Santana, C.J., Jr., Soares, L.S.L., Bezerra, S.G.T.A., Bastos-Filho, C.J.A., et al.
"Metaheuristics for feature selection in handwritten digit recognition"
. 2nd Latin-America Congress on Computational Intelligence, LA-CCI 2015, 2016.
http://dx.doi.org/10.1109/LA-CCI.2015.7435975---------- VANCOUVER ----------
Seijas, L.M., Carneiro, R.F., Santana, C.J., Jr., Soares, L.S.L., Bezerra, S.G.T.A., Bastos-Filho, C.J.A., et al. Metaheuristics for feature selection in handwritten digit recognition. Latin-Am. Congr. Comput. Intell., LA-CCI. 2016.
http://dx.doi.org/10.1109/LA-CCI.2015.7435975