Statistical Comparisons of the Top 10 Algorithms in Data Mining for Classification Task

TitleStatistical Comparisons of the Top 10 Algorithms in Data Mining for Classification Task
Publication TypeJournal Article
Year of Publication2016
AuthorsSettouti, N., M. E. A. Bechar, and M. A. Chikh
JournalInternational Journal of Interactive Multimedia and Artificial Intelligence
ISSN1989-1660
IssueSpecial Issue on Artificial Intelligence Underpinning
Volume4
Number1
Date Published09/2016
Pagination46-51
Abstract

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.

KeywordsAlgorithms, Classification, Data Mining, Friefman Test, Test
DOI10.9781/ijimai.2016.419
URLhttp://www.ijimai.org/JOURNAL/sites/default/files/files/2016/02/ijimai20164_1_9_pdf_19943.pdf
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