LAMDA-HAD, an Extension to the LAMDA Classifier in the Context of Supervised Learning

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dc.citation.journalTitleINTERNATIONAL JOURNAL OF INFORMATION TECHNOLOGY & DECISION MAKINGeng
dc.contributor.authorMorales L.
dc.contributor.authorAguilar J.
dc.contributor.authorChávez D.
dc.contributor.authorIsaza C.
dc.contributor.departmentUniversidad EAFIT. Departamento de Ingeniería de Sistemasspa
dc.contributor.researchgroupI+D+I en Tecnologías de la Información y las Comunicacionesspa
dc.creatorMorales L.
dc.creatorAguilar J.
dc.creatorChávez D.
dc.creatorIsaza C.
dc.date.accessioned2021-04-12T20:55:48Z
dc.date.available2021-04-12T20:55:48Z
dc.date.issued2020-01-01
dc.description.abstractThis paper proposes a new approach to improve the performance of Learning Algorithm for Multivariable Data Analysis (LAMDA). This algorithm can be used for supervised and unsupervised learning, based on the calculation of the Global Adequacy Degree (GAD) of one individual to a class, through the contributions of all its descriptors. LAMDA has the capability of creating new classes after the training stage. If an individual does not have enough similarity to the preexisting classes, it is evaluated with respect to a threshold called the Non-Informative Class (NIC), this being the novelty of the algorithm. However, LAMDA has problems making good classifications, either because the NIC is constant for all classes, or because the GAD calculation is unreliable. In this work, its efficiency is improved by two strategies, the first one, by the calculation of adaptable NICs for each class, which prevents that correctly classified individuals create new classes; and the second one, by computing the Higher Adequacy Degree (HAD), which grants more robustness to the algorithm. LAMDA-HAD is validated by applying it in different benchmarks and comparing it with LAMDA and other classifiers, through a statistical analysis to determinate the cases in which our algorithm presents a better performance. © 2019 World Scientific Publishing Company.eng
dc.identifierhttps://eafit.fundanetsuite.com/Publicaciones/ProdCientif/PublicacionFrw.aspx?id=10278
dc.identifier.doi10.1142/S0219622019500457
dc.identifier.issn02196220
dc.identifier.issn17936845
dc.identifier.otherWOS;000522158400011
dc.identifier.otherSCOPUS;2-s2.0-85078972759
dc.identifier.urihttp://hdl.handle.net/10784/28635
dc.language.isoengeng
dc.publisherWorld Scientific Publishing Co
dc.relationDOI;10.1142/S0219622019500457
dc.relationWOS;000522158400011
dc.relationSCOPUS;2-s2.0-85078972759
dc.relation.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85078972759&doi=10.1142%2fS0219622019500457&partnerID=40&md5=610e8bbbc719e1f2ec16f7a3c77c7d90
dc.rightshttps://v2.sherpa.ac.uk/id/publication/issn/0219-6220
dc.sourceINTERNATIONAL JOURNAL OF INFORMATION TECHNOLOGY & DECISION MAKING
dc.subjectBenchmarkingeng
dc.subjectMachine learningeng
dc.subjectNegative impedance converterseng
dc.subjectSupervised learningeng
dc.subjectadequacy degreeeng
dc.subjectDescriptorseng
dc.subjectFuzzy classificationeng
dc.subjectIts efficiencieseng
dc.subjectLAMDAeng
dc.subjectLearning algorithm for multivariable data analysiseng
dc.subjectNew approacheseng
dc.subjectSupervised and unsupervised learningeng
dc.subjectLearning algorithmseng
dc.titleLAMDA-HAD, an Extension to the LAMDA Classifier in the Context of Supervised Learningeng
dc.typeinfo:eu-repo/semantics/articleeng
dc.typearticleeng
dc.typeinfo:eu-repo/semantics/publishedVersioneng
dc.typepublishedVersioneng
dc.type.localArtículospa

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