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dc.creatorSanz Delgado, José Antonioes_ES
dc.creatorFernández, Albertoes_ES
dc.creatorBustince Sola, Humbertoes_ES
dc.creatorHerrera, Franciscoes_ES
dc.date.accessioned2015-07-27T09:55:13Z
dc.date.issued2012
dc.identifier.issn1793-6411 (electronic)
dc.identifier.urihttps://hdl.handle.net/2454/17685
dc.descriptionElectronic 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/ijufksen
dc.description.abstractThe 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.en
dc.description.sponsorshipThis work was supported in part by the Spanish Ministry of Science and Technology under projects TIN2011-28488 and TIN2010-15055.en
dc.format.mimetypeapplication/pdfen
dc.language.isoengen
dc.publisherWorld Scientific Publishing Companyen
dc.relation.ispartofInternational Journal of Uncertainty, Fuzziness and Knowledge-Based Systems Vol. 20, Suppl. 2 (October 2012) 1–30en
dc.rights© World Scientific Publishing Companyen
dc.subjectLinguistic fuzzy rule-based classification systemsen
dc.subjectInterval-valued fuzzy setsen
dc.subjectIgnorance functionsen
dc.subjectTuningen
dc.subjectFuzzy decision treesen
dc.subjectClassificationen
dc.titleIIVFDT: ignorance functions based interval-valued fuzzy decision tree with genetic tuningen
dc.typeArtículo / Artikuluaes
dc.typeinfo:eu-repo/semantics/articleen
dc.contributor.departmentUniversidad Pública de Navarra. Departamento de Automática y Computaciónes_ES
dc.contributor.departmentNafarroako Unibertsitate Publikoa. Automatika eta Konputazioa Sailaeu
dc.rights.accessRightsAcceso abierto / Sarbide irekiaes
dc.rights.accessRightsinfo:eu-repo/semantics/openAccessen
dc.identifier.doi10.1142/S0218488512400132
dc.relation.projectIDinfo:eu-repo/grantAgreement/ES/6PN/TIN2011-28488en
dc.relation.projectIDinfo:eu-repo/grantAgreement/ES/6PN/TIN2010-15055en
dc.relation.publisherversionhttps://dx.doi.org/10.1142/S0218488512400132
dc.type.versionVersión aceptada / Onetsi den bertsioaes
dc.type.versioninfo:eu-repo/semantics/acceptedVersionen


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