A hybrid machine learning system to impute and classify a component-based robot

In the field of cybernetic systems and more specifically in robotics, one of the fundamental objectives is the detection of anomalies in order to minimize loss of time. Following this idea, this paper proposes the implementation of a Hybrid Intelligent System in four steps to impute the missing valu...

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Detalhes bibliográficos
Autores: Basurto Hornillos, Nuño, Arroyo Puente, Ángel, Cambra Baseca, Carlos, Herrero Cosío, Álvaro
Formato: artículo
Estado:Versión aceptada para publicación
Fecha de publicación:2022
País:España
Recursos:Universidad de Burgos (UBU)
Repositorio:Repositorio Institucional de la Universidad de Burgos (RIUBU)
OAI Identifier:oai:riubu.ubu.es:10259/8248
Acesso em linha:http://hdl.handle.net/10259/8248
Access Level:acceso abierto
Palavra-chave:Hybrid Artificial Intelligence System
Machine learning
Clustering
Regression
Missing values
Component-Based Robot
Informática
Computer science
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spelling A hybrid machine learning system to impute and classify a component-based robotBasurto Hornillos, NuñoArroyo Puente, ÁngelCambra Baseca, CarlosHerrero Cosío, ÁlvaroHybrid Artificial Intelligence SystemMachine learningClusteringRegressionMissing valuesComponent-Based RobotInformáticaComputer scienceIn the field of cybernetic systems and more specifically in robotics, one of the fundamental objectives is the detection of anomalies in order to minimize loss of time. Following this idea, this paper proposes the implementation of a Hybrid Intelligent System in four steps to impute the missing values, by combining clustering and regression techniques, followed by balancing and classification tasks. This system applies regression models to each one of the clusters built on the instances of data set. Subsequently, a variety of balancing techniques are applied to improve the classifier’s ability to discern whether it is in an error or a normal state. These techniques support to obtain better classification ratios in which a robot is close to error and allow us to bring the behavior back to a normal state. The experimentation is performed using a modern and public data set, which has been extracted from a component-based robotic system, in which different anomalies are induced by software in their components.Oxford University Press202420242022info:eu-repo/semantics/articleinfo:eu-repo/semantics/acceptedVersionapplication/pdfhttp://hdl.handle.net/10259/8248reponame:Repositorio Institucional de la Universidad de Burgos (RIUBU)instname:Universidad de Burgos (UBU)InglésLogic Journal of the IGPL. 2023, V. 31, n. 2, p. 338-351https://doi.org/10.1093/jigpal/jzac023info:eu-repo/semantics/openAccessoai:riubu.ubu.es:10259/82482026-05-28T07:56:11Z
dc.title.none.fl_str_mv A hybrid machine learning system to impute and classify a component-based robot
title A hybrid machine learning system to impute and classify a component-based robot
spellingShingle A hybrid machine learning system to impute and classify a component-based robot
Basurto Hornillos, Nuño
Hybrid Artificial Intelligence System
Machine learning
Clustering
Regression
Missing values
Component-Based Robot
Informática
Computer science
title_short A hybrid machine learning system to impute and classify a component-based robot
title_full A hybrid machine learning system to impute and classify a component-based robot
title_fullStr A hybrid machine learning system to impute and classify a component-based robot
title_full_unstemmed A hybrid machine learning system to impute and classify a component-based robot
title_sort A hybrid machine learning system to impute and classify a component-based robot
dc.creator.none.fl_str_mv Basurto Hornillos, Nuño
Arroyo Puente, Ángel
Cambra Baseca, Carlos
Herrero Cosío, Álvaro
author Basurto Hornillos, Nuño
author_facet Basurto Hornillos, Nuño
Arroyo Puente, Ángel
Cambra Baseca, Carlos
Herrero Cosío, Álvaro
author_role author
author2 Arroyo Puente, Ángel
Cambra Baseca, Carlos
Herrero Cosío, Álvaro
author2_role author
author
author
dc.subject.none.fl_str_mv Hybrid Artificial Intelligence System
Machine learning
Clustering
Regression
Missing values
Component-Based Robot
Informática
Computer science
topic Hybrid Artificial Intelligence System
Machine learning
Clustering
Regression
Missing values
Component-Based Robot
Informática
Computer science
description In the field of cybernetic systems and more specifically in robotics, one of the fundamental objectives is the detection of anomalies in order to minimize loss of time. Following this idea, this paper proposes the implementation of a Hybrid Intelligent System in four steps to impute the missing values, by combining clustering and regression techniques, followed by balancing and classification tasks. This system applies regression models to each one of the clusters built on the instances of data set. Subsequently, a variety of balancing techniques are applied to improve the classifier’s ability to discern whether it is in an error or a normal state. These techniques support to obtain better classification ratios in which a robot is close to error and allow us to bring the behavior back to a normal state. The experimentation is performed using a modern and public data set, which has been extracted from a component-based robotic system, in which different anomalies are induced by software in their components.
publishDate 2022
dc.date.none.fl_str_mv 2022
2024
2024
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/acceptedVersion
format article
status_str acceptedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/10259/8248
url http://hdl.handle.net/10259/8248
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv Logic Journal of the IGPL. 2023, V. 31, n. 2, p. 338-351
https://doi.org/10.1093/jigpal/jzac023
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Oxford University Press
publisher.none.fl_str_mv Oxford University Press
dc.source.none.fl_str_mv reponame:Repositorio Institucional de la Universidad de Burgos (RIUBU)
instname:Universidad de Burgos (UBU)
instname_str Universidad de Burgos (UBU)
reponame_str Repositorio Institucional de la Universidad de Burgos (RIUBU)
collection Repositorio Institucional de la Universidad de Burgos (RIUBU)
repository.name.fl_str_mv
repository.mail.fl_str_mv
_version_ 1869417777897406464
score 15,301603

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