IIVFDT: ignorance functions based interval-valued fuzzy decision tree with genetic tuning

Electronic version of an article published as International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems Vol. 20, Suppl. 2 (October 2012) 1–30 DOI: 10.1142/S0218488512400132 © World Scientific Publishing Company http://www.worldscientific.com/worldscinet/ijufks

Detalles Bibliográficos
Autores: Sanz Delgado, José Antonio, Fernández, Alberto, Bustince Sola, Humberto, Herrera, Francisco
Tipo de recurso: artículo
Estado:Versión aceptada para publicación
Fecha de publicación:2012
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:2454/17685
Acceso en línea:https://hdl.handle.net/2454/17685
Access Level:acceso abierto
Palabra clave:Linguistic fuzzy rule-based classification systems
Interval-valued fuzzy sets
Ignorance functions
Tuning
Fuzzy decision trees
Classification
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oai_identifier_str oai:academica-e.unavarra.es:2454/17685
network_acronym_str ES
network_name_str España
repository_id_str
dc.title.none.fl_str_mv IIVFDT: ignorance functions based interval-valued fuzzy decision tree with genetic tuning
title IIVFDT: ignorance functions based interval-valued fuzzy decision tree with genetic tuning
spellingShingle IIVFDT: ignorance functions based interval-valued fuzzy decision tree with genetic tuning
Sanz Delgado, José Antonio
Linguistic fuzzy rule-based classification systems
Interval-valued fuzzy sets
Ignorance functions
Tuning
Fuzzy decision trees
Classification
title_short IIVFDT: ignorance functions based interval-valued fuzzy decision tree with genetic tuning
title_full IIVFDT: ignorance functions based interval-valued fuzzy decision tree with genetic tuning
title_fullStr IIVFDT: ignorance functions based interval-valued fuzzy decision tree with genetic tuning
title_full_unstemmed IIVFDT: ignorance functions based interval-valued fuzzy decision tree with genetic tuning
title_sort IIVFDT: ignorance functions based interval-valued fuzzy decision tree with genetic tuning
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 Linguistic fuzzy rule-based classification systems
Interval-valued fuzzy sets
Ignorance functions
Tuning
Fuzzy decision trees
Classification
topic Linguistic fuzzy rule-based classification systems
Interval-valued fuzzy sets
Ignorance functions
Tuning
Fuzzy decision trees
Classification
description Electronic version of an article published as International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems Vol. 20, Suppl. 2 (October 2012) 1–30 DOI: 10.1142/S0218488512400132 © World Scientific Publishing Company http://www.worldscientific.com/worldscinet/ijufks
publishDate 2012
dc.date.none.fl_str_mv 2012
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/2454/17685
url https://hdl.handle.net/2454/17685
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv info:eu-repo/grantAgreement/MICINN//TIN2011-28488
info:eu-repo/grantAgreement/MICINN//TIN2010-15055
dc.rights.none.fl_str_mv © World Scientific Publishing Company
info:eu-repo/semantics/openAccess
rights_invalid_str_mv © World Scientific Publishing Company
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv World Scientific Publishing Company
publisher.none.fl_str_mv World Scientific Publishing Company
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
_version_ 1869408402821611520
spelling IIVFDT: ignorance functions based interval-valued fuzzy decision tree with genetic tuningSanz Delgado, José AntonioFernández, AlbertoBustince Sola, HumbertoHerrera, FranciscoLinguistic fuzzy rule-based classification systemsInterval-valued fuzzy setsIgnorance functionsTuningFuzzy decision treesClassificationElectronic version of an article published as International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems Vol. 20, Suppl. 2 (October 2012) 1–30 DOI: 10.1142/S0218488512400132 © World Scientific Publishing Company http://www.worldscientific.com/worldscinet/ijufksThe choice of membership functions plays an essential role in the success of fuzzy systems. This is a complex problem due to the possible lack of knowledge when assigning punctual values as membership degrees. To face this handicap, we propose a methodology called Ignorance functions based Interval-Valued Fuzzy Decision Tree with genetic tuning, IIVFDT for short, which allows to improve the performance of fuzzy decision trees by taking into account the ignorance degree. This ignorance degree is the result of a weak ignorance function applied to the punctual value set as membership degree. Our IIVFDT proposal is composed of four steps: (1) the base fuzzy decision tree is generated using the fuzzy ID3 algorithm; (2) the linguistic labels are modeled with Interval-Valued Fuzzy Sets. To do so, a new parametrized construction method of Interval-Valued Fuzzy Sets is defined, whose length represents such ignorance degree; (3) the fuzzy reasoning method is extended to work with this representation of the linguistic terms; (4) an evolutionary tuning step is applied for computing the optimal ignorance degree for each Interval-Valued Fuzzy Set. The experimental study shows that the IIVFDT method allows the results provided by the initial fuzzy ID3 with and without Interval-Valued Fuzzy Sets to be outperformed. The suitability of the proposed methodology is shown with respect to both several state-of-the-art fuzzy decision trees and C4.5. Furthermore, we analyze the quality of our approach versus two methods that learn the fuzzy decision tree using genetic algorithms. Finally, we show that a superior performance can be achieved by means of the positive synergy obtained when applying the well known genetic tuning of the lateral position after the application of the IIVFDT method. The choice of membership functions plays an essential role in the success of fuzzy systems. This is a complex problem due to the possible lack of knowledge when assigning punctual values as membership degrees. To face this handicap, we propose a methodology called Ignorance functions based Interval-Valued Fuzzy Decision Tree with genetic tuning, IIVFDT for short, which allows to improve the performance of fuzzy decision trees by taking into account the ignorance degree. This ignorance degree is the result of a weak ignorance function applied to the punctual value set as membership degree. Our IIVFDT proposal is composed of four steps: (1) the base fuzzy decision tree is generated using the fuzzy ID3 algorithm; (2) the linguistic labels are modeled with Interval-Valued Fuzzy Sets. To do so, a new parametrized construction method of Interval-Valued Fuzzy Sets is defined, whose length represents such ignorance degree; (3) the fuzzy reasoning method is extended to work with this representation of the linguistic terms; (4) an evolutionary tuning step is applied for computing the optimal ignorance degree for each Interval-Valued Fuzzy Set. The experimental study shows that the IIVFDT method allows the results provided by the initial fuzzy ID3 with and without Interval-Valued Fuzzy Sets to be outperformed. The suitability of the proposed methodology is shown with respect to both several state-of-the-art fuzzy decision trees and C4.5. Furthermore, we analyze the quality of our approach versus two methods that learn the fuzzy decision tree using genetic algorithms. Finally, we show that a superior performance can be achieved by means of the positive synergy obtained when applying the well known genetic tuning of the lateral position after the application of the IIVFDT method.This work was supported in part by the Spanish Ministry of Science and Technology under projects TIN2011-28488 and TIN2010-15055.World Scientific Publishing CompanyAutomática y ComputaciónAutomatika eta Konputazioa2012info:eu-repo/semantics/articleinfo:eu-repo/semantics/acceptedVersionapplication/pdfhttps://hdl.handle.net/2454/17685reponame:Academica-e. Repositorio Institucional de la Universidad Pública de Navarrainstname:Universidad Pública de NavarraInglésinfo:eu-repo/grantAgreement/MICINN//TIN2011-28488info:eu-repo/grantAgreement/MICINN//TIN2010-15055© World Scientific Publishing Companyinfo:eu-repo/semantics/openAccessoai:academica-e.unavarra.es:2454/176852026-06-17T12:41:47Z
score 15,300724

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