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ThinkMind // International Journal On Advances in Software, volume 5, numbers 1 and 2, 2012 // View article soft_v5_n12_2012_1


Combining Explicitness and Classifying Performance via MIDOVA Lossless Representation for Qualitative Datasets

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

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