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

• Main Authors: Nesma Settouti, Mohammed El Amine Bechar, Mohammed Amine Chikh
• Format: Article
• Language: English
• Published: Universidad Internacional de La Rioja (UNIR) 2016-09-01
• Series: International Journal of Interactive Multimedia and Artificial Intelligence
• Subjects: Algorithms; Classification; Data Mining; Friefman Test; Test
• Online Access: http://www.ijimai.org/journal/node/911

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.