Optimising Multilayer Perceptron weights and biases through a Cellular Genetic Algorithm for medical data classification

Autores
Rojas, Matias Gabriel; Olivera, Ana Carolina; Vidal, Pablo Javier
Año de publicación
2022
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
In recent years, technology in medicine has shown a significant advance due to artificial intelligence becoming a framework to make accurate medical diagnoses. Models like Multilayer Perceptrons (MLPs) can detect implicit patterns in data, allowing identifying patients conditions that cannot be seen easily. MLPs consist of biased neurons arranged in layers, connected by weighted connections. Their effectiveness depends on finding the optimal weights and biases that reduce the classification error, which is usually done by using the Back Propagation algorithm (BP). But BP has several disadvantages that could provoke the MLP not to learn. Metaheuristics are alternatives to BP that reach high-quality solutions without using many computational resources. In this work, the Cellular Genetic Algorithm (CGA) with a specially designed crossover operator called Damped Crossover (DX), is proposed to optimise weights and biases of the MLP to classify medical data. When compared against state-of-the-art algorithms, the CGA configured with DX obtained the minimal Mean Square Error value in three out of the five considered medical datasets and was the quickest algorithm with four datasets, showing a better balance between time consumed and optimisation performance. Additionally, it is competitive in enhancing classification quality, reaching the best accuracy with two datasets and the second-best accuracy with two of the remaining.
Fil: Rojas, Matias Gabriel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mendoza; Argentina. Universidad Nacional de Cuyo. Instituto para las Tecnologías de la Información y las Comunicaciones; Argentina
Fil: Olivera, Ana Carolina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mendoza; Argentina. Universidad Nacional de Cuyo. Instituto para las Tecnologías de la Información y las Comunicaciones; Argentina. Universidad Nacional de Cuyo. Facultad de Ingeniería; Argentina
Fil: Vidal, Pablo Javier. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mendoza; Argentina. Universidad Nacional de Cuyo. Instituto para las Tecnologías de la Información y las Comunicaciones; Argentina. Universidad Nacional de Cuyo. Facultad de Ingeniería; Argentina
Materia
CELLULAR GENETIC ALGORITHM
MEDICAL DATA CLASSIFICATION
METAHEURISTICS
MULTILAYER PERCEPTRON
TRAINING METHODS
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/187877
Acceder
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oai_identifier_str oai:ri.conicet.gov.ar:11336/187877
network_acronym_str CONICETDig
repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling Optimising Multilayer Perceptron weights and biases through a Cellular Genetic Algorithm for medical data classificationRojas, Matias GabrielOlivera, Ana CarolinaVidal, Pablo JavierCELLULAR GENETIC ALGORITHMMEDICAL DATA CLASSIFICATIONMETAHEURISTICSMULTILAYER PERCEPTRONTRAINING METHODShttps://purl.org/becyt/ford/1.2https://purl.org/becyt/ford/1In recent years, technology in medicine has shown a significant advance due to artificial intelligence becoming a framework to make accurate medical diagnoses. Models like Multilayer Perceptrons (MLPs) can detect implicit patterns in data, allowing identifying patients conditions that cannot be seen easily. MLPs consist of biased neurons arranged in layers, connected by weighted connections. Their effectiveness depends on finding the optimal weights and biases that reduce the classification error, which is usually done by using the Back Propagation algorithm (BP). But BP has several disadvantages that could provoke the MLP not to learn. Metaheuristics are alternatives to BP that reach high-quality solutions without using many computational resources. In this work, the Cellular Genetic Algorithm (CGA) with a specially designed crossover operator called Damped Crossover (DX), is proposed to optimise weights and biases of the MLP to classify medical data. When compared against state-of-the-art algorithms, the CGA configured with DX obtained the minimal Mean Square Error value in three out of the five considered medical datasets and was the quickest algorithm with four datasets, showing a better balance between time consumed and optimisation performance. Additionally, it is competitive in enhancing classification quality, reaching the best accuracy with two datasets and the second-best accuracy with two of the remaining.Fil: Rojas, Matias Gabriel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mendoza; Argentina. Universidad Nacional de Cuyo. Instituto para las Tecnologías de la Información y las Comunicaciones; ArgentinaFil: Olivera, Ana Carolina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mendoza; Argentina. Universidad Nacional de Cuyo. Instituto para las Tecnologías de la Información y las Comunicaciones; Argentina. Universidad Nacional de Cuyo. Facultad de Ingeniería; ArgentinaFil: Vidal, Pablo Javier. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mendoza; Argentina. Universidad Nacional de Cuyo. Instituto para las Tecnologías de la Información y las Comunicaciones; Argentina. Universidad Nacional de Cuyo. Facultad de Ingeniería; ArgentinaElsevier2022-07info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/187877Rojas, Matias Gabriel; Olivera, Ana Carolina; Vidal, Pablo Javier; Optimising Multilayer Perceptron weights and biases through a Cellular Genetic Algorithm for medical data classification; Elsevier; Array; 14; 7-2022; 1-152590-0056CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/https://linkinghub.elsevier.com/retrieve/pii/S2590005622000339info:eu-repo/semantics/altIdentifier/doi/10.1016/j.array.2022.100173info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-nd/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-03T10:06:40Zoai:ri.conicet.gov.ar:11336/187877instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-09-03 10:06:40.875CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Optimising Multilayer Perceptron weights and biases through a Cellular Genetic Algorithm for medical data classification
title Optimising Multilayer Perceptron weights and biases through a Cellular Genetic Algorithm for medical data classification
spellingShingle Optimising Multilayer Perceptron weights and biases through a Cellular Genetic Algorithm for medical data classification
Rojas, Matias Gabriel
CELLULAR GENETIC ALGORITHM
MEDICAL DATA CLASSIFICATION
METAHEURISTICS
MULTILAYER PERCEPTRON
TRAINING METHODS
title_short Optimising Multilayer Perceptron weights and biases through a Cellular Genetic Algorithm for medical data classification
title_full Optimising Multilayer Perceptron weights and biases through a Cellular Genetic Algorithm for medical data classification
title_fullStr Optimising Multilayer Perceptron weights and biases through a Cellular Genetic Algorithm for medical data classification
title_full_unstemmed Optimising Multilayer Perceptron weights and biases through a Cellular Genetic Algorithm for medical data classification
title_sort Optimising Multilayer Perceptron weights and biases through a Cellular Genetic Algorithm for medical data classification
dc.creator.none.fl_str_mv Rojas, Matias Gabriel
Olivera, Ana Carolina
Vidal, Pablo Javier
author Rojas, Matias Gabriel
author_facet Rojas, Matias Gabriel
Olivera, Ana Carolina
Vidal, Pablo Javier
author_role author
author2 Olivera, Ana Carolina
Vidal, Pablo Javier
author2_role author
author
dc.subject.none.fl_str_mv CELLULAR GENETIC ALGORITHM
MEDICAL DATA CLASSIFICATION
METAHEURISTICS
MULTILAYER PERCEPTRON
TRAINING METHODS
topic CELLULAR GENETIC ALGORITHM
MEDICAL DATA CLASSIFICATION
METAHEURISTICS
MULTILAYER PERCEPTRON
TRAINING METHODS
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.2
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv In recent years, technology in medicine has shown a significant advance due to artificial intelligence becoming a framework to make accurate medical diagnoses. Models like Multilayer Perceptrons (MLPs) can detect implicit patterns in data, allowing identifying patients conditions that cannot be seen easily. MLPs consist of biased neurons arranged in layers, connected by weighted connections. Their effectiveness depends on finding the optimal weights and biases that reduce the classification error, which is usually done by using the Back Propagation algorithm (BP). But BP has several disadvantages that could provoke the MLP not to learn. Metaheuristics are alternatives to BP that reach high-quality solutions without using many computational resources. In this work, the Cellular Genetic Algorithm (CGA) with a specially designed crossover operator called Damped Crossover (DX), is proposed to optimise weights and biases of the MLP to classify medical data. When compared against state-of-the-art algorithms, the CGA configured with DX obtained the minimal Mean Square Error value in three out of the five considered medical datasets and was the quickest algorithm with four datasets, showing a better balance between time consumed and optimisation performance. Additionally, it is competitive in enhancing classification quality, reaching the best accuracy with two datasets and the second-best accuracy with two of the remaining.
Fil: Rojas, Matias Gabriel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mendoza; Argentina. Universidad Nacional de Cuyo. Instituto para las Tecnologías de la Información y las Comunicaciones; Argentina
Fil: Olivera, Ana Carolina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mendoza; Argentina. Universidad Nacional de Cuyo. Instituto para las Tecnologías de la Información y las Comunicaciones; Argentina. Universidad Nacional de Cuyo. Facultad de Ingeniería; Argentina
Fil: Vidal, Pablo Javier. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mendoza; Argentina. Universidad Nacional de Cuyo. Instituto para las Tecnologías de la Información y las Comunicaciones; Argentina. Universidad Nacional de Cuyo. Facultad de Ingeniería; Argentina
description In recent years, technology in medicine has shown a significant advance due to artificial intelligence becoming a framework to make accurate medical diagnoses. Models like Multilayer Perceptrons (MLPs) can detect implicit patterns in data, allowing identifying patients conditions that cannot be seen easily. MLPs consist of biased neurons arranged in layers, connected by weighted connections. Their effectiveness depends on finding the optimal weights and biases that reduce the classification error, which is usually done by using the Back Propagation algorithm (BP). But BP has several disadvantages that could provoke the MLP not to learn. Metaheuristics are alternatives to BP that reach high-quality solutions without using many computational resources. In this work, the Cellular Genetic Algorithm (CGA) with a specially designed crossover operator called Damped Crossover (DX), is proposed to optimise weights and biases of the MLP to classify medical data. When compared against state-of-the-art algorithms, the CGA configured with DX obtained the minimal Mean Square Error value in three out of the five considered medical datasets and was the quickest algorithm with four datasets, showing a better balance between time consumed and optimisation performance. Additionally, it is competitive in enhancing classification quality, reaching the best accuracy with two datasets and the second-best accuracy with two of the remaining.
publishDate 2022
dc.date.none.fl_str_mv 2022-07
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/11336/187877
Rojas, Matias Gabriel; Olivera, Ana Carolina; Vidal, Pablo Javier; Optimising Multilayer Perceptron weights and biases through a Cellular Genetic Algorithm for medical data classification; Elsevier; Array; 14; 7-2022; 1-15
2590-0056
CONICET Digital
CONICET
url http://hdl.handle.net/11336/187877
identifier_str_mv Rojas, Matias Gabriel; Olivera, Ana Carolina; Vidal, Pablo Javier; Optimising Multilayer Perceptron weights and biases through a Cellular Genetic Algorithm for medical data classification; Elsevier; Array; 14; 7-2022; 1-15
2590-0056
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/https://linkinghub.elsevier.com/retrieve/pii/S2590005622000339
info:eu-repo/semantics/altIdentifier/doi/10.1016/j.array.2022.100173
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv Elsevier
publisher.none.fl_str_mv Elsevier
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
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score 13.13397

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