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    Por favor, use este identificador para citar o enlazar este ítem:https://uvadoc.uva.es/handle/10324/74371

    Título
    Checking Orthogonal Transformations and Genetic Algorithms for Selection of Fuzzy Rules based on Interpretability-Accuracy Concepts
    Autor
    Rey, M. Isabel
    Galende Hernández, MartaAutoridad UVA Orcid
    Fuente Aparicio, María Jesús de laAutoridad UVA Orcid
    Sáinz Palmero, Gregorio IsmaelAutoridad UVA Orcid
    Año del Documento
    2012
    Editorial
    World Scientific
    Descripción
    Producción Científica
    Documento Fuente
    International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 2012 20:supp02, 159-186
    Résumé
    Fuzzy modeling is one of the most known and used techniques in different areas to model the behavior of systems and processes. In most cases, as in data-driven fuzzy modeling, these fuzzy models reach a high performance from the point of view of accuracy, but from other points of view, such as complexity or interpretability, they can present a poor performance. Several approaches are found in the bibliography to reduce the complexity and improve the interpretability of the fuzzy models. In this paper, a post-processing approach is carried out via rule selection, whose aim is to choose the most relevant rules for working together on the well-known accuracy-interpretability trade-off. The rule relevancy is based on Orthogonal Transformations, such as the SVD-QR rank revealing approach, the P-QR and OLS transformations. Rule selection is carried out using a genetic algorithm that takes into account the information obtained by the Orthogonal Transformations. The main objective is to check the true significance, drawbacks and advantages of the rule selection based on the orthogonal transformations via the rule firing strength matrix. In order to carry out this aim, a neuro-fuzzy system, FasArt (Fuzzy Adaptive System ART based), and several case studies, data sets from the KEEL Project Repository, are used to tune and check this selection of rules based on orthogonal transformations, genetic selection and accuracy-interpretability trade-off. This neuro-fuzzy system generates Mamdani fuzzy rule based systems (FRBSs), in an approximative way. NSGA-II is the MOEA tool used to tune the proposed rule selection.
    Palabras Clave
    Fuzzy systems
    Interpretability
    Accuracy
    Rule selection
    Orthogonal transformations
    Genetic algorithm
    ISSN
    0218-4885
    Revisión por pares
    SI
    DOI
    10.1142/S0218488512400193
    Patrocinador
    Spanish Ministry of Science and Innovation under grants no. DPI2009-14410-C02-02 and IPT-2011-1656-370000
    Version del Editor
    https://www.worldscientific.com/doi/abs/10.1142/S0218488512400193
    Propietario de los Derechos
    World Scientific Publishing Company
    Idioma
    eng
    URI
    https://uvadoc.uva.es/handle/10324/74371
    Tipo de versión
    info:eu-repo/semantics/acceptedVersion
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    • DEP44 - Artículos de revista [78]
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    2012_IJUFKBS_ReyGalendeFuenteSainz_v14.pdf
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    Attribution 4.0 InternacionalExcepté là où spécifié autrement, la license de ce document est décrite en tant que Attribution 4.0 Internacional

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