01533nas a2200241   4500000000100000000000100001008004100002260001200043653001600055653001900071653000900090653001500099653001800114100001900132700002900151700002500180245009200205856009800297300001000395490000600405520086600411022001401277       2016                            d  c09/201610aData Mining10aClassification10aTest10aAlgorithms10aFriefman Test1 aNesma Settouti1 aMohammed El Amine Bechar1 aMohammed Amine Chikh00aStatistical Comparisons of the Top 10 Algorithms in Data Mining for Classification Task  uhttp://www.ijimai.org/JOURNAL/sites/default/files/files/2016/02/ijimai20164_1_9_pdf_19943.pdf  a46-510 v43 aThis 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.  a1989-1660