Artificial bee colony optimization for feature selection of traffic sign recognition

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 selecti...

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Autores principales: Da Silva, D.L., Seijas, L.M., Bastos-Filho, C.J.A.
Formato: JOUR
Materias:
Artificial bee colony
Classification
Feature selection
Random forest
Swarm intelligence
Traffic sign recognition
Acceso en línea:http://hdl.handle.net/20.500.12110/paper_19479263_v8_n2_p50_DaSilva
Aporte de:
Biblioteca Digital - Facultad de Ciencias Exactas y Naturales (UBA) de Universidad de Buenos Aires
id todo:paper_19479263_v8_n2_p50_DaSilva
record_format dspace
spelling todo:paper_19479263_v8_n2_p50_DaSilva2023-10-03T16:37:08Z Artificial bee colony optimization for feature selection of traffic sign recognition Da Silva, D.L. Seijas, L.M. Bastos-Filho, C.J.A. Artificial bee colony Classification Feature selection Random forest Swarm intelligence Traffic sign recognition 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. JOUR info:eu-repo/semantics/openAccess http://creativecommons.org/licenses/by/2.5/ar http://hdl.handle.net/20.500.12110/paper_19479263_v8_n2_p50_DaSilva
institution Universidad de Buenos Aires
institution_str I-28
repository_str R-134
collection Biblioteca Digital - Facultad de Ciencias Exactas y Naturales (UBA)
topic Artificial bee colony
Classification
Feature selection
Random forest
Swarm intelligence
Traffic sign recognition
spellingShingle Artificial bee colony
Classification
Feature selection
Random forest
Swarm intelligence
Traffic sign recognition
Da Silva, D.L.
Seijas, L.M.
Bastos-Filho, C.J.A.
Artificial bee colony optimization for feature selection of traffic sign recognition
topic_facet Artificial bee colony
Classification
Feature selection
Random forest
Swarm intelligence
Traffic sign recognition
description 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.
format JOUR
author Da Silva, D.L.
Seijas, L.M.
Bastos-Filho, C.J.A.
author_facet Da Silva, D.L.
Seijas, L.M.
Bastos-Filho, C.J.A.
author_sort Da Silva, D.L.
title Artificial bee colony optimization for feature selection of traffic sign recognition
title_short Artificial bee colony optimization for feature selection of traffic sign recognition
title_full Artificial bee colony optimization for feature selection of traffic sign recognition
title_fullStr Artificial bee colony optimization for feature selection of traffic sign recognition
title_full_unstemmed Artificial bee colony optimization for feature selection of traffic sign recognition
title_sort artificial bee colony optimization for feature selection of traffic sign recognition
url http://hdl.handle.net/20.500.12110/paper_19479263_v8_n2_p50_DaSilva
work_keys_str_mv AT dasilvadl artificialbeecolonyoptimizationforfeatureselectionoftrafficsignrecognition
AT seijaslm artificialbeecolonyoptimizationforfeatureselectionoftrafficsignrecognition
AT bastosfilhocja artificialbeecolonyoptimizationforfeatureselectionoftrafficsignrecognition
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