Adaptive decision making systems

Autores
Bulacio, Pilar; Magdalena, Luis; Tapia, Elizabeth
Año de publicación
2005
Idioma
inglés
Tipo de recurso
documento de conferencia
Estado
versión publicada
Descripción
Given a population of classifiers, we consider the problem of designing highly compact and error adaptive decision making systems. A selection approach based on misclassification diversity and potential cooperation among classifiers is proposed. The compactness constraint allows us the efficient implementation of fuzzy integral combination rules regarding both the interpretability of fuzzy measures and low complexity of fuzzy integral operator. Experimental results show the feasibility of our approach.
VI Workshop de Agentes y Sistemas Inteligentes (WASI)
Red de Universidades con Carreras en Informática (RedUNCI)
Materia
Ciencias Informáticas
scalability
multiclassifier system
fuzzy integral
Logic Programming
Nivel de accesibilidad
acceso abierto
Condiciones de uso
http://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Repositorio
SEDICI (UNLP)
Institución
Universidad Nacional de La Plata
OAI Identificador
oai:sedici.unlp.edu.ar:10915/22982
Acceder
id SEDICI_c87e7b5e15e380985747759fcef382e2
oai_identifier_str oai:sedici.unlp.edu.ar:10915/22982
network_acronym_str SEDICI
repository_id_str 1329
network_name_str SEDICI (UNLP)
spelling Adaptive decision making systemsBulacio, PilarMagdalena, LuisTapia, ElizabethCiencias Informáticasscalabilitymulticlassifier systemfuzzy integralLogic ProgrammingGiven a population of classifiers, we consider the problem of designing highly compact and error adaptive decision making systems. A selection approach based on misclassification diversity and potential cooperation among classifiers is proposed. The compactness constraint allows us the efficient implementation of fuzzy integral combination rules regarding both the interpretability of fuzzy measures and low complexity of fuzzy integral operator. Experimental results show the feasibility of our approach.VI Workshop de Agentes y Sistemas Inteligentes (WASI)Red de Universidades con Carreras en Informática (RedUNCI)2005-10info:eu-repo/semantics/conferenceObjectinfo:eu-repo/semantics/publishedVersionObjeto de conferenciahttp://purl.org/coar/resource_type/c_5794info:ar-repo/semantics/documentoDeConferenciaapplication/pdfhttp://sedici.unlp.edu.ar/handle/10915/22982enginfo:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by-nc-sa/2.5/ar/Creative Commons Attribution-NonCommercial-ShareAlike 2.5 Argentina (CC BY-NC-SA 2.5)reponame:SEDICI (UNLP)instname:Universidad Nacional de La Platainstacron:UNLP2025-10-22T16:36:47Zoai:sedici.unlp.edu.ar:10915/22982Institucionalhttp://sedici.unlp.edu.ar/Universidad públicaNo correspondehttp://sedici.unlp.edu.ar/oai/snrdalira@sedici.unlp.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:13292025-10-22 16:36:47.603SEDICI (UNLP) - Universidad Nacional de La Platafalse
dc.title.none.fl_str_mv Adaptive decision making systems
title Adaptive decision making systems
spellingShingle Adaptive decision making systems
Bulacio, Pilar
Ciencias Informáticas
scalability
multiclassifier system
fuzzy integral
Logic Programming
title_short Adaptive decision making systems
title_full Adaptive decision making systems
title_fullStr Adaptive decision making systems
title_full_unstemmed Adaptive decision making systems
title_sort Adaptive decision making systems
dc.creator.none.fl_str_mv Bulacio, Pilar
Magdalena, Luis
Tapia, Elizabeth
author Bulacio, Pilar
author_facet Bulacio, Pilar
Magdalena, Luis
Tapia, Elizabeth
author_role author
author2 Magdalena, Luis
Tapia, Elizabeth
author2_role author
author
dc.subject.none.fl_str_mv Ciencias Informáticas
scalability
multiclassifier system
fuzzy integral
Logic Programming
topic Ciencias Informáticas
scalability
multiclassifier system
fuzzy integral
Logic Programming
dc.description.none.fl_txt_mv Given a population of classifiers, we consider the problem of designing highly compact and error adaptive decision making systems. A selection approach based on misclassification diversity and potential cooperation among classifiers is proposed. The compactness constraint allows us the efficient implementation of fuzzy integral combination rules regarding both the interpretability of fuzzy measures and low complexity of fuzzy integral operator. Experimental results show the feasibility of our approach.
VI Workshop de Agentes y Sistemas Inteligentes (WASI)
Red de Universidades con Carreras en Informática (RedUNCI)
description Given a population of classifiers, we consider the problem of designing highly compact and error adaptive decision making systems. A selection approach based on misclassification diversity and potential cooperation among classifiers is proposed. The compactness constraint allows us the efficient implementation of fuzzy integral combination rules regarding both the interpretability of fuzzy measures and low complexity of fuzzy integral operator. Experimental results show the feasibility of our approach.
publishDate 2005
dc.date.none.fl_str_mv 2005-10
dc.type.none.fl_str_mv info:eu-repo/semantics/conferenceObject
info:eu-repo/semantics/publishedVersion
Objeto de conferencia
http://purl.org/coar/resource_type/c_5794
info:ar-repo/semantics/documentoDeConferencia
format conferenceObject
status_str publishedVersion
dc.identifier.none.fl_str_mv http://sedici.unlp.edu.ar/handle/10915/22982
url http://sedici.unlp.edu.ar/handle/10915/22982
dc.language.none.fl_str_mv eng
language eng
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
http://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Creative Commons Attribution-NonCommercial-ShareAlike 2.5 Argentina (CC BY-NC-SA 2.5)
eu_rights_str_mv openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Creative Commons Attribution-NonCommercial-ShareAlike 2.5 Argentina (CC BY-NC-SA 2.5)
dc.format.none.fl_str_mv application/pdf
dc.source.none.fl_str_mv reponame:SEDICI (UNLP)
instname:Universidad Nacional de La Plata
instacron:UNLP
reponame_str SEDICI (UNLP)
collection SEDICI (UNLP)
instname_str Universidad Nacional de La Plata
instacron_str UNLP
institution UNLP
repository.name.fl_str_mv SEDICI (UNLP) - Universidad Nacional de La Plata
repository.mail.fl_str_mv alira@sedici.unlp.edu.ar
_version_ 1846782826325737472
score 12.982451

Publicaciones similares