Application-Independent Feature Construction Based onAlmost-Closedness Properties

Feature construction has been studied extensively, including for 0/1 data samples. Given the recent breakthroughs in closedness-related constraint-based mining, we are considering its impact on feature construction for classification tasks. We investigate the use of condensed representations of freq...

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Bibliographic Details
Published in:Knowledge and information systems 2011-03, Vol.30, p.87-111
Main Authors: Gay, Dominique, Selmaoui-Folcher, Nazha, Boulicaut, Jean-François
Format: Article
Language:English
Subjects:
Computer Science
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Summary:Feature construction has been studied extensively, including for 0/1 data samples. Given the recent breakthroughs in closedness-related constraint-based mining, we are considering its impact on feature construction for classification tasks. We investigate the use of condensed representations of frequent itemsets based on closedness properties as new features. These itemset types have been proposed to avoid set counting in difficult association rule mining tasks, i.e., when datais noisy and/or highly correlated. However, our guess is that their intrinsic properties (say the maximality for the closed itemsets and the minimality for the delta-free itemsets) should have an impact on feature quality.Understanding this remains fairly open and we discuss these issues thanks to itemset properties on the one hand and an experimental validation on various data sets (possibly noisy) on the other hand.
ISSN:0219-1377
0219-3116