Combining Explicitness and Classifying Performance via MIDOVA Lossless Representation for Qualitative Datasets
Identifieur interne : 001C95 ( Main/Exploration ); précédent : 001C94; suivant : 001C96Combining Explicitness and Classifying Performance via MIDOVA Lossless Representation for Qualitative Datasets
Auteurs : Martine Cadot [France] ; Alain Lelu [France]Source :
- International Journal On Advances in Software [ 1942-2628 ] ; 2012.
English descriptors
- mix :
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.
Url:
Affiliations:
- France
- Grand Est, Lorraine (région)
- Nancy
- Institut national polytechnique de Lorraine, Université Nancy 2, Université de Lorraine
Links toward previous steps (curation, corpus...)
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Le document en format XML
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extraction</term><term>machine learning</term><term>non-linear discrimination</term><term>symbolic discrimination</term><term>user comprehensibility</term><term>variable interaction</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">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.</div></front></TEI><affiliations><list><country><li>France</li></country><region><li>Grand Est</li><li>Lorraine (région)</li></region><settlement><li>Nancy</li></settlement><orgName><li>Institut national polytechnique de Lorraine</li><li>Université Nancy 2</li><li>Université de Lorraine</li></orgName></list><tree><country name="France"><region name="Grand Est"><name sortKey="Cadot, Martine" sort="Cadot, Martine" uniqKey="Cadot M" first="Martine" last="Cadot">Martine Cadot</name></region><name sortKey="Lelu, Alain" sort="Lelu, Alain" uniqKey="Lelu A" first="Alain" last="Lelu">Alain Lelu</name></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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