Home // International Journal On Advances in Software, volume 5, numbers 1 and 2, 2012 // View article
Authors:
Martine Cadot
Alain Lelu
Keywords: symbolic discrimination; variable interaction; machine learning; classification; non-linear discrimination; user comprehensibility; feature construction; feature selection; itemset extraction
Abstract:
Basically, MIDOVA lists the relevant combinations of K boolean variables, thus giving rise to an appropriate expansion of the original set of variables, well-fitted to for a number of data mining tasks. MIDOVA takes into account the presence as well as the absence of items. The building of level-k itemsets starting from level-k-1 ones relies on the concept of residue, which entails the potential of an itemset to create higher-order non-trivial associations. We assess the value of such a representation by presenting an application to three well-known classification tasks: the resulting success proves that our objective of extracting the relevant interactions hidden in the data, and only these ones, has been hit.
Pages: 1 to 14
Copyright: Copyright (c) to authors, 2012. Used with permission.
Publication date: June 30, 2012
Published in: journal
ISSN: 1942-2628