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    • Revista IJIMAI
    • 2016
    • vol. 4, nº 1, september 2016
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    • Revista IJIMAI
    • 2016
    • vol. 4, nº 1, september 2016
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    Statistical Comparisons of the Top 10 Algorithms in Data Mining for Classification Task

    Autor: 
    Settouti, Nesma
    ;
    El Amine Bechar, Mohammed
    ;
    Amine Chikh, Mohammed
    Fecha: 
    09/2016
    Palabra clave: 
    data mining; classification; test; algorithms; friefman test; IJIMAI
    Tipo de Ítem: 
    article
    URI: 
    https://reunir.unir.net/handle/123456789/11573
    DOI: 
    http://doi.org/10.9781/ijimai.2016.419
    Dirección web: 
    https://ijimai.org/journal/bibcite/reference/2527
    Open Access
    Resumen:
    This work is builds on the study of the 10 top data mining algorithms identified by the IEEE International Conference on Data Mining (ICDM) community in December 2006. We address the same study, but with the application of statistical tests to establish, a more appropriate and justified ranking classifier for classification tasks. Current studies and practices on theoretical and empirical comparison of several methods, approaches, advocated tests that are more appropriate. Thereby, recent studies recommend a set of simple and robust non-parametric tests for statistical comparisons classifiers. In this paper, we propose to perform non-parametric statistical tests by the Friedman test with post-hoc tests corresponding to the comparison of several classifiers on multiple data sets. The tests provide a better judge for the relevance of these algorithms.
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    Nombre: ijimai20164_1_9_pdf_19943.pdf
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