Fuzzy heterogeneous neurons for imprecise classification problems

In the classical neuron model, inputs are continuous real-valued quantities. However, in many important domains from the real world, objects are described by a mixture of continuous and discrete variables, usually containing missing information and uncertainty. In this paper, a general class of neur...

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Autores: Valdés Ramos, Julio José, Belanche Muñoz, Luis Antonio|||0000-0002-7577-1964, Alquézar Mancho, René|||0000-0002-6420-0517
Formato: artículo
Fecha de publicación:2000
País:España
Recursos:Universitat Politècnica de Catalunya (UPC)
Repositorio:UPCommons. Portal del coneixement obert de la UPC
Idioma:inglés
OAI Identifier:oai:upcommons.upc.edu:2117/182212
Acesso em linha:https://hdl.handle.net/2117/182212
https://dx.doi.org/10.1002/(SICI)1098-111X(200003)15:3<265::AID-INT7>3.0.CO;2-I
Access Level:acceso abierto
Palavra-chave:Fuzzy systems
Neural networks (Computer science)
Machine learning
Environmental science computing
Fuzzy neural nets
Learning (artificial intelligence)
Pattern classification
Uncertainty handling
Sistemes borrosos
Xarxes neuronals (Informàtica)
Aprenentatge automàtic
Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial::Aprenentatge automàtic
id ES_e0bffe13af8da4f4e33920e1304f2cdc
oai_identifier_str oai:upcommons.upc.edu:2117/182212
network_acronym_str ES
network_name_str España
repository_id_str
spelling Fuzzy heterogeneous neurons for imprecise classification problemsValdés Ramos, Julio JoséBelanche Muñoz, Luis Antonio|||0000-0002-7577-1964Alquézar Mancho, René|||0000-0002-6420-0517Fuzzy systemsNeural networks (Computer science)Machine learningEnvironmental science computingFuzzy neural netsLearning (artificial intelligence)Pattern classificationUncertainty handlingSistemes borrososXarxes neuronals (Informàtica)Aprenentatge automàticÀrees temàtiques de la UPC::Informàtica::Intel·ligència artificial::Aprenentatge automàticIn the classical neuron model, inputs are continuous real-valued quantities. However, in many important domains from the real world, objects are described by a mixture of continuous and discrete variables, usually containing missing information and uncertainty. In this paper, a general class of neuron models accepting heterogeneous inputs in the form of mixtures of continuous (crisp and/or fuzzy) and discrete quantities admitting missing data is presented. From these, several particular models can be derived as instances and different neural architectures constructed with them. Such models deal in a natural way with problems for which information is imprecise or even missing. Their possibilities in classification and diagnostic problems are here illustrated by experiments with data from a real-world domain in the field of environmental studies. These experiments show that such neurons can both learn and classify complex data very effectively in the presence of uncertain information.Peer ReviewedWiley20002000-02-0120202020-03-30journal articlehttp://purl.org/coar/resource_type/c_6501AMhttp://purl.org/coar/version/c_ab4af688f83e57aainfo:eu-repo/semantics/articleapplication/pdfhttps://hdl.handle.net/2117/182212https://dx.doi.org/10.1002/(SICI)1098-111X(200003)15:3<265::AID-INT7>3.0.CO;2-Ireponame:UPCommons. Portal del coneixement obert de la UPCinstname:Universitat Politècnica de Catalunya (UPC)Inglésengopen accesshttp://purl.org/coar/access_right/c_abf2info:eu-repo/semantics/openAccessoai:upcommons.upc.edu:2117/1822122026-05-27T15:37:01Z
dc.title.none.fl_str_mv Fuzzy heterogeneous neurons for imprecise classification problems
title Fuzzy heterogeneous neurons for imprecise classification problems
spellingShingle Fuzzy heterogeneous neurons for imprecise classification problems
Valdés Ramos, Julio José
Fuzzy systems
Neural networks (Computer science)
Machine learning
Environmental science computing
Fuzzy neural nets
Learning (artificial intelligence)
Pattern classification
Uncertainty handling
Sistemes borrosos
Xarxes neuronals (Informàtica)
Aprenentatge automàtic
Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial::Aprenentatge automàtic
title_short Fuzzy heterogeneous neurons for imprecise classification problems
title_full Fuzzy heterogeneous neurons for imprecise classification problems
title_fullStr Fuzzy heterogeneous neurons for imprecise classification problems
title_full_unstemmed Fuzzy heterogeneous neurons for imprecise classification problems
title_sort Fuzzy heterogeneous neurons for imprecise classification problems
dc.creator.none.fl_str_mv Valdés Ramos, Julio José
Belanche Muñoz, Luis Antonio|||0000-0002-7577-1964
Alquézar Mancho, René|||0000-0002-6420-0517
author Valdés Ramos, Julio José
author_facet Valdés Ramos, Julio José
Belanche Muñoz, Luis Antonio|||0000-0002-7577-1964
Alquézar Mancho, René|||0000-0002-6420-0517
author_role author
author2 Belanche Muñoz, Luis Antonio|||0000-0002-7577-1964
Alquézar Mancho, René|||0000-0002-6420-0517
author2_role author
author
dc.subject.none.fl_str_mv Fuzzy systems
Neural networks (Computer science)
Machine learning
Environmental science computing
Fuzzy neural nets
Learning (artificial intelligence)
Pattern classification
Uncertainty handling
Sistemes borrosos
Xarxes neuronals (Informàtica)
Aprenentatge automàtic
Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial::Aprenentatge automàtic
topic Fuzzy systems
Neural networks (Computer science)
Machine learning
Environmental science computing
Fuzzy neural nets
Learning (artificial intelligence)
Pattern classification
Uncertainty handling
Sistemes borrosos
Xarxes neuronals (Informàtica)
Aprenentatge automàtic
Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial::Aprenentatge automàtic
description In the classical neuron model, inputs are continuous real-valued quantities. However, in many important domains from the real world, objects are described by a mixture of continuous and discrete variables, usually containing missing information and uncertainty. In this paper, a general class of neuron models accepting heterogeneous inputs in the form of mixtures of continuous (crisp and/or fuzzy) and discrete quantities admitting missing data is presented. From these, several particular models can be derived as instances and different neural architectures constructed with them. Such models deal in a natural way with problems for which information is imprecise or even missing. Their possibilities in classification and diagnostic problems are here illustrated by experiments with data from a real-world domain in the field of environmental studies. These experiments show that such neurons can both learn and classify complex data very effectively in the presence of uncertain information.
publishDate 2000
dc.date.none.fl_str_mv 2000
2000-02-01
2020
2020-03-30
dc.type.none.fl_str_mv journal article
http://purl.org/coar/resource_type/c_6501
AM
http://purl.org/coar/version/c_ab4af688f83e57aa
dc.type.openaire.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv https://hdl.handle.net/2117/182212
https://dx.doi.org/10.1002/(SICI)1098-111X(200003)15:3<265::AID-INT7>3.0.CO;2-I
url https://hdl.handle.net/2117/182212
https://dx.doi.org/10.1002/(SICI)1098-111X(200003)15:3<265::AID-INT7>3.0.CO;2-I
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Wiley
publisher.none.fl_str_mv Wiley
dc.source.none.fl_str_mv reponame:UPCommons. Portal del coneixement obert de la UPC
instname:Universitat Politècnica de Catalunya (UPC)
instname_str Universitat Politècnica de Catalunya (UPC)
reponame_str UPCommons. Portal del coneixement obert de la UPC
collection UPCommons. Portal del coneixement obert de la UPC
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15,300719

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