A comparative study of several genetic-based supervised learning systems

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

    Research output: Book chapterChapterpeer-review

    6 Citations (Scopus)

    Abstract

    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.

    Original languageEnglish
    Title of host publicationLearning Classifier Systems in Data Mining
    EditorsLarry Bull, Ester Bernadó-Mansilla, John Holmes
    Pages205-230
    Number of pages26
    DOIs
    Publication statusPublished - 2008

    Publication series

    NameStudies in Computational Intelligence
    Volume125
    ISSN (Print)1860-949X

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