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dc.contributor.authorSettouti, Nesma
dc.contributor.authorEl Amine Bechar, Mohammed
dc.contributor.authorAmine Chikh, Mohammed
dc.date2016-09
dc.date.accessioned2021-07-07T12:30:51Z
dc.date.available2021-07-07T12:30:51Z
dc.identifier.issn1989-1660
dc.identifier.urihttps://reunir.unir.net/handle/123456789/11573
dc.description.abstractThis 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.es_ES
dc.language.isoenges_ES
dc.publisherInternational Journal of Interactive Multimedia and Artificial Intelligence (IJIMAI)es_ES
dc.relation.ispartofseries;vol. 4, nº 1
dc.relation.urihttps://ijimai.org/journal/bibcite/reference/2527es_ES
dc.rightsopenAccesses_ES
dc.subjectdata mininges_ES
dc.subjectclassificationes_ES
dc.subjecttestes_ES
dc.subjectalgorithmses_ES
dc.subjectfriefman testes_ES
dc.subjectIJIMAIes_ES
dc.titleStatistical Comparisons of the Top 10 Algorithms in Data Mining for Classification Taskes_ES
dc.typearticlees_ES
reunir.tag~IJIMAIes_ES
dc.identifier.doihttp://doi.org/10.9781/ijimai.2016.419


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