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dc.contributor.authorSanz, José Antonio
dc.contributor.authorBustince, Humberto
dc.contributor.authorFernández Hilario, Alberto Luis 
dc.contributor.authorHerrera Triguero, Francisco 
dc.date.accessioned2021-01-21T12:00:02Z
dc.date.available2021-01-21T12:00:02Z
dc.date.issued2012
dc.identifier.citationPublisher version: Sanz, J., Bustince, H., Fernández, A., & Herrera, F. (2012). IIVFDT: Ignorance functions based interval-valued fuzzy decision tree with genetic tuning. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 20(supp02), 1-30 [https://doi.org/10.1142/S0218488512400132]es_ES
dc.identifier.urihttp://hdl.handle.net/10481/65892
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.es_ES
dc.description.sponsorshipSpanish Government TIN2011-28488 TIN2010-15055es_ES
dc.language.isoenges_ES
dc.publisherWorld Scientific Publishinges_ES
dc.rightsAtribución-NoComercial-SinDerivadas 3.0 España*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/es/*
dc.subjectLinguistic Fuzzy Rule-Based Classification Systemses_ES
dc.subjectIntervalValued Fuzzy Setses_ES
dc.subjectIgnorance functionses_ES
dc.subjectTuning fuzzy decision treeses_ES
dc.subjectClassification es_ES
dc.titleIIVFDT: Ignorance Functions based Interval-Valued Fuzzy Decision Tree with Genetic Tuninges_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.rights.accessRightsinfo:eu-repo/semantics/openAccesses_ES
dc.identifier.doi10.1142/S0218488512400132


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Atribución-NoComercial-SinDerivadas 3.0 España
Except where otherwise noted, this item's license is described as Atribución-NoComercial-SinDerivadas 3.0 España