Artículo

Da Silva, D.L.; Seijas, L.M.; Bastos-Filho, C.J.A. "Artificial bee colony optimization for feature selection of traffic sign recognition" (2017) International Journal of Swarm Intelligence Research. 8(2):50-67
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Abstract:

This paper proposes the application of a swarm intelligence algorithm called Artificial Bee Colony (ABC) for the feature selection to feed a Random Forest (RF) classifier aiming to recognise Traffic Signs. In this paper, the authors define and assess several fitness functions for the feature selection stage. The idea is to minimise the correlation and maximise the entropy of a set of masks to be used for feature extraction results in a higher information gain and allows to reach recognition accuracies comparable with other state-of-art algorithms. The RF comprises as a committee based on decision trees, which allows handling large datasets and features with high performance, enabling a Traffic Sign Recognition (TSR) system oriented for real-time implementations. The German Traffic Sign Recognition Benchmark (GTSRB) was used for experiments, serving as a real basis for comparison of performance for the authors' proposal. © 2017, IGI Global.

Registro:

Documento: Artículo
Título:Artificial bee colony optimization for feature selection of traffic sign recognition
Autor:Da Silva, D.L.; Seijas, L.M.; Bastos-Filho, C.J.A.
Filiación:Polytechnic School of Pernambuco, University of Pernambuco (UPE), Recife, Brazil
Federal Institute of Pernambuco (IFPE), Palmares, Brazil
Departamento de Computación, Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires, Buenos Aires, Argentina
Palabras clave:Artificial bee colony; Classification; Feature selection; Random forest; Swarm intelligence; Traffic sign recognition
Año:2017
Volumen:8
Número:2
Página de inicio:50
Página de fin:67
DOI: http://dx.doi.org/10.4018/IJSIR.2017040104
Título revista:International Journal of Swarm Intelligence Research
Título revista abreviado:Int. J. Swarm Intelligence Res.
ISSN:19479263
Registro:https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_19479263_v8_n2_p50_DaSilva

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Citas:

---------- APA ----------
Da Silva, D.L., Seijas, L.M. & Bastos-Filho, C.J.A. (2017) . Artificial bee colony optimization for feature selection of traffic sign recognition. International Journal of Swarm Intelligence Research, 8(2), 50-67.
http://dx.doi.org/10.4018/IJSIR.2017040104
---------- CHICAGO ----------
Da Silva, D.L., Seijas, L.M., Bastos-Filho, C.J.A. "Artificial bee colony optimization for feature selection of traffic sign recognition" . International Journal of Swarm Intelligence Research 8, no. 2 (2017) : 50-67.
http://dx.doi.org/10.4018/IJSIR.2017040104
---------- MLA ----------
Da Silva, D.L., Seijas, L.M., Bastos-Filho, C.J.A. "Artificial bee colony optimization for feature selection of traffic sign recognition" . International Journal of Swarm Intelligence Research, vol. 8, no. 2, 2017, pp. 50-67.
http://dx.doi.org/10.4018/IJSIR.2017040104
---------- VANCOUVER ----------
Da Silva, D.L., Seijas, L.M., Bastos-Filho, C.J.A. Artificial bee colony optimization for feature selection of traffic sign recognition. Int. J. Swarm Intelligence Res. 2017;8(2):50-67.
http://dx.doi.org/10.4018/IJSIR.2017040104