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

Martine Cadot 1 Alain Lelu 2, 3, 4
1 ABC - Machine Learning and Computational Biology
LORIA - Laboratoire Lorrain de Recherche en Informatique et ses Applications
2 KIWI - Knowledge Information and Web Intelligence
LORIA - Laboratoire Lorrain de Recherche en Informatique et ses Applications
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.
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https://hal.archives-ouvertes.fr/hal-00596718
Contributor : Martine Cadot <>
Submitted on : Sunday, May 29, 2011 - 7:58:55 PM
Last modification on : Friday, April 2, 2021 - 3:33:24 AM

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Martine Cadot, Alain Lelu. Combining Explicitness and Classifying Performance via MIDOVA Lossless Representation for Qualitative Datasets. International Journal On Advances in Software, IARIA, 2012, 5 (1&2), pp.1-16. ⟨hal-00596718⟩

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