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dc.contributor.authorBasurto Hornillos, Nuño 
dc.contributor.authorArroyo Puente, Ángel 
dc.contributor.authorCambra Baseca, Carlos 
dc.contributor.authorHerrero Cosío, Álvaro 
dc.date.accessioned2024-01-08T13:24:56Z
dc.date.available2024-01-08T13:24:56Z
dc.date.issued2022-02
dc.identifier.issn1367-0751
dc.identifier.urihttp://hdl.handle.net/10259/8248
dc.description.abstractIn 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.en
dc.format.mimetypeapplication/pdf
dc.language.isoenges
dc.publisherOxford University Pressen
dc.relation.ispartofLogic Journal of the IGPL. 2023, V. 31, n. 2, p. 338-351es
dc.subjectHybrid Artificial Intelligence Systemen
dc.subjectMachine learningen
dc.subjectClusteringen
dc.subjectRegressionen
dc.subjectMissing valuesen
dc.subjectComponent-Based Roboten
dc.subject.otherInformáticaes
dc.subject.otherComputer scienceen
dc.titleA hybrid machine learning system to impute and classify a component-based roboten
dc.typeinfo:eu-repo/semantics/articlees
dc.rights.accessRightsinfo:eu-repo/semantics/openAccesses
dc.relation.publisherversionhttps://doi.org/10.1093/jigpal/jzac023es
dc.identifier.doi10.1093/jigpal/jzac023
dc.identifier.essn1368-9894
dc.journal.titleLogic Journal of the IGPLen
dc.volume.number31es
dc.issue.number2es
dc.page.initial338es
dc.page.final351es
dc.type.hasVersioninfo:eu-repo/semantics/acceptedVersiones


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