Towards UCI+: A mindful repository design

Núria Macià, Ester Bernadó-Mansilla

    Producció científica: Article en revista indexadaArticleAvaluat per experts

    50 Cites (Scopus)


    Public repositories have contributed to the maturation of experimental methodology in machine learning. Publicly available data sets have allowed researchers to empirically assess their learners and, jointly with open source machine learning software, they have favoured the emergence of comparative analyses of learners' performance over a common framework. These studies have brought standard procedures to evaluate machine learning techniques. However, current claims - such as the superiority of enhanced algorithms - are biased by unsustained assumptions made throughout some praxes. In this paper, the early steps of the methodology, which refer to data set selection, are inspected. Particularly, the exploitation of the most popular data repository in machine learning - the UCI repository - is examined. We analyse the type, complexity, and use of UCI data sets. The study recommends the design of a mindful data repository, UCI+, which should include a set of properly characterised data sets consisting of a complete and representative sample of real-world problems, enriched with artificial benchmarks. The ultimate goal of the UCI+ is to lay the foundations towards a well-supported methodology for learner assessment.

    Idioma originalAnglès
    Pàgines (de-a)237-262
    Nombre de pàgines26
    RevistaInformation Sciences
    Estat de la publicacióPublicada - 10 de març 2014


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