Metaheuristics for feature selection in handwritten digit recognition
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 cl...
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| Acceso en línea: | https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_97814673_v_n_p_Seijas http://hdl.handle.net/20.500.12110/paper_97814673_v_n_p_Seijas |
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paper:paper_97814673_v_n_p_Seijas2023-06-08T16:37:43Z Metaheuristics for feature selection in handwritten digit recognition Seijas, Leticia María 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) 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. Fil:Seijas, L.M. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales; Argentina. 2016 https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_97814673_v_n_p_Seijas http://hdl.handle.net/20.500.12110/paper_97814673_v_n_p_Seijas |
| institution |
Universidad de Buenos Aires |
| institution_str |
I-28 |
| repository_str |
R-134 |
| collection |
Biblioteca Digital - Facultad de Ciencias Exactas y Naturales (UBA) |
| topic |
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) |
| spellingShingle |
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) Seijas, Leticia María Metaheuristics for feature selection in handwritten digit recognition |
| topic_facet |
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) |
| description |
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. |
| author |
Seijas, Leticia María |
| author_facet |
Seijas, Leticia María |
| author_sort |
Seijas, Leticia María |
| title |
Metaheuristics for feature selection in handwritten digit recognition |
| title_short |
Metaheuristics for feature selection in handwritten digit recognition |
| title_full |
Metaheuristics for feature selection in handwritten digit recognition |
| title_fullStr |
Metaheuristics for feature selection in handwritten digit recognition |
| title_full_unstemmed |
Metaheuristics for feature selection in handwritten digit recognition |
| title_sort |
metaheuristics for feature selection in handwritten digit recognition |
| publishDate |
2016 |
| url |
https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_97814673_v_n_p_Seijas http://hdl.handle.net/20.500.12110/paper_97814673_v_n_p_Seijas |
| work_keys_str_mv |
AT seijasleticiamaria metaheuristicsforfeatureselectioninhandwrittendigitrecognition |
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1768543011243819008 |