A comparative study of several genetic-based supervised learning systems

Albert Orriols-Puig, Jorge Casillas, Ester Bernadó-Mansilla

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    Resumen

    This chapter gives insight in the use of Genetic-Based Machine Learning (GBML) for supervised tasks. Five GBML systems which represent different learning methodologies and knowledge representations in the GBML paradigm are selected for the analysis: UCS, GAssist, SLAVE, Fuzzy AdaBoost, and Fuzzy LogitBoost. UCS and GAssist are based on a non-fuzzy representation, while SLAVE, Fuzzy AdaBoost, and Fuzzy LogitBoost use a linguistic fuzzy representation. The models evolved by these five systems are compared in terms of performance and interpretability to the models created by six highly-used non-evolutionary learners. Experimental observations highlight the suitability of GBML systems for classification tasks. Moreover, the analysis points out which systems should be used depending on whether the user prefers to maximize the accuracy or the interpretability of the models.

    Idioma originalInglés
    Título de la publicación alojadaLearning Classifier Systems in Data Mining
    EditoresLarry Bull, Ester Bernadó-Mansilla, John Holmes
    Páginas205-230
    Número de páginas26
    DOI
    EstadoPublicada - 2008

    Serie de la publicación

    NombreStudies in Computational Intelligence
    Volumen125
    ISSN (versión impresa)1860-949X

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