A genetic tuning to improve the performance of fuzzy rule-based classification systems with interval-valued fuzzy sets: degree of ignorance and lateral position

Fuzzy Rule-Based Systems are appropriate tools to deal with classification problems due to their good properties. However, they can suffer a lack of system accuracy as a result of the uncertainty inherent in the definition of the membership functions and the limitation of the homogeneous distributio...

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Autores: Sanz Delgado, José Antonio, Fernández, Alberto, Bustince Sola, Humberto, Herrera, Francisco
Tipo de recurso: artículo
Estado:Versión aceptada para publicación
Fecha de publicación:2011
País:España
Institución:Universidad Pública de Navarra
Repositorio:Academica-e. Repositorio Institucional de la Universidad Pública de Navarra
OAI Identifier:oai:academica-e.unavarra.es:2454/17684
Acceso en línea:https://hdl.handle.net/2454/17684
Access Level:acceso abierto
Palabra clave:Fuzzy rule-based classification systems
Interval-valued fuzzy sets
Ignorance functions
Linguistic 2-tuples representation
Genetic fuzzy systems
Tuning
Genetic algorithms
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spelling A genetic tuning to improve the performance of fuzzy rule-based classification systems with interval-valued fuzzy sets: degree of ignorance and lateral positionSanz Delgado, José AntonioFernández, AlbertoBustince Sola, HumbertoHerrera, FranciscoFuzzy rule-based classification systemsInterval-valued fuzzy setsIgnorance functionsLinguistic 2-tuples representationGenetic fuzzy systemsTuningGenetic algorithmsFuzzy Rule-Based Systems are appropriate tools to deal with classification problems due to their good properties. However, they can suffer a lack of system accuracy as a result of the uncertainty inherent in the definition of the membership functions and the limitation of the homogeneous distribution of the linguistic labels. The aim of the paper is to improve the performance of Fuzzy Rule-Based Classification Systems by means of the Theory of Interval-Valued Fuzzy Sets and a post-processing genetic tuning step. In order to build the Interval-Valued Fuzzy Sets we define a new function called weak ignorance for modeling the uncertainty associated with the definition of the membership functions. Next, we adapt the fuzzy partitions to the problem in an optimal way through a cooperative evolutionary tuning in which we handle both the degree of ignorance and the lateral position (based on the 2-tuples fuzzy linguistic representation) of the linguistic labels. The experimental study is carried out over a large collection of data-sets and it is supported by a statistical analysis. Our results show empirically that the use of our methodology outperforms the initial Fuzzy-Rule Based Classification System. The application of our cooperative tuning enhances the results provided by the use of the isolated tuning approaches and also improves the behavior of the genetic tuning based on the 3-tuples fuzzy linguistic representation.This work was supported in part by the Spanish Ministry of Science and Technology under projects TIN2008-06681-C06-01 and TIN2010-15055.ElsevierAutomática y ComputaciónAutomatika eta Konputazioa2011info:eu-repo/semantics/articleinfo:eu-repo/semantics/acceptedVersionapplication/pdfhttps://hdl.handle.net/2454/17684reponame:Academica-e. Repositorio Institucional de la Universidad Pública de Navarrainstname:Universidad Pública de NavarraInglés© 2011 Elsevier Inc. The manuscript version is made available under the CC BY-NC-ND 4.0 licensehttps://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccessoai:academica-e.unavarra.es:2454/176842026-06-17T12:41:47Z
dc.title.none.fl_str_mv A genetic tuning to improve the performance of fuzzy rule-based classification systems with interval-valued fuzzy sets: degree of ignorance and lateral position
title A genetic tuning to improve the performance of fuzzy rule-based classification systems with interval-valued fuzzy sets: degree of ignorance and lateral position
spellingShingle A genetic tuning to improve the performance of fuzzy rule-based classification systems with interval-valued fuzzy sets: degree of ignorance and lateral position
Sanz Delgado, José Antonio
Fuzzy rule-based classification systems
Interval-valued fuzzy sets
Ignorance functions
Linguistic 2-tuples representation
Genetic fuzzy systems
Tuning
Genetic algorithms
title_short A genetic tuning to improve the performance of fuzzy rule-based classification systems with interval-valued fuzzy sets: degree of ignorance and lateral position
title_full A genetic tuning to improve the performance of fuzzy rule-based classification systems with interval-valued fuzzy sets: degree of ignorance and lateral position
title_fullStr A genetic tuning to improve the performance of fuzzy rule-based classification systems with interval-valued fuzzy sets: degree of ignorance and lateral position
title_full_unstemmed A genetic tuning to improve the performance of fuzzy rule-based classification systems with interval-valued fuzzy sets: degree of ignorance and lateral position
title_sort A genetic tuning to improve the performance of fuzzy rule-based classification systems with interval-valued fuzzy sets: degree of ignorance and lateral position
dc.creator.none.fl_str_mv Sanz Delgado, José Antonio
Fernández, Alberto
Bustince Sola, Humberto
Herrera, Francisco
author Sanz Delgado, José Antonio
author_facet Sanz Delgado, José Antonio
Fernández, Alberto
Bustince Sola, Humberto
Herrera, Francisco
author_role author
author2 Fernández, Alberto
Bustince Sola, Humberto
Herrera, Francisco
author2_role author
author
author
dc.contributor.none.fl_str_mv Automática y Computación
Automatika eta Konputazioa
dc.subject.none.fl_str_mv Fuzzy rule-based classification systems
Interval-valued fuzzy sets
Ignorance functions
Linguistic 2-tuples representation
Genetic fuzzy systems
Tuning
Genetic algorithms
topic Fuzzy rule-based classification systems
Interval-valued fuzzy sets
Ignorance functions
Linguistic 2-tuples representation
Genetic fuzzy systems
Tuning
Genetic algorithms
description Fuzzy Rule-Based Systems are appropriate tools to deal with classification problems due to their good properties. However, they can suffer a lack of system accuracy as a result of the uncertainty inherent in the definition of the membership functions and the limitation of the homogeneous distribution of the linguistic labels. The aim of the paper is to improve the performance of Fuzzy Rule-Based Classification Systems by means of the Theory of Interval-Valued Fuzzy Sets and a post-processing genetic tuning step. In order to build the Interval-Valued Fuzzy Sets we define a new function called weak ignorance for modeling the uncertainty associated with the definition of the membership functions. Next, we adapt the fuzzy partitions to the problem in an optimal way through a cooperative evolutionary tuning in which we handle both the degree of ignorance and the lateral position (based on the 2-tuples fuzzy linguistic representation) of the linguistic labels. The experimental study is carried out over a large collection of data-sets and it is supported by a statistical analysis. Our results show empirically that the use of our methodology outperforms the initial Fuzzy-Rule Based Classification System. The application of our cooperative tuning enhances the results provided by the use of the isolated tuning approaches and also improves the behavior of the genetic tuning based on the 3-tuples fuzzy linguistic representation.
publishDate 2011
dc.date.none.fl_str_mv 2011
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 https://hdl.handle.net/2454/17684
url https://hdl.handle.net/2454/17684
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.rights.none.fl_str_mv © 2011 Elsevier Inc. The manuscript version is made available under the CC BY-NC-ND 4.0 license
https://creativecommons.org/licenses/by-nc-nd/4.0/
info:eu-repo/semantics/openAccess
rights_invalid_str_mv © 2011 Elsevier Inc. The manuscript version is made available under the CC BY-NC-ND 4.0 license
https://creativecommons.org/licenses/by-nc-nd/4.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Elsevier
publisher.none.fl_str_mv Elsevier
dc.source.none.fl_str_mv reponame:Academica-e. Repositorio Institucional de la Universidad Pública de Navarra
instname:Universidad Pública de Navarra
instname_str Universidad Pública de Navarra
reponame_str Academica-e. Repositorio Institucional de la Universidad Pública de Navarra
collection Academica-e. Repositorio Institucional de la Universidad Pública de Navarra
repository.name.fl_str_mv
repository.mail.fl_str_mv
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