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2007
IEEE

Feature Space Enrichment by Incorporation of Implicit Features for Effective Classification

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Feature Space Enrichment by Incorporation of Implicit Features for Effective Classification
Feature Space Conversion for classifiers is the process by which the data that is to be fed into the classifier is transformed from one form to another. The motivation behind doing this is to enhance the “discriminative power” of the data together with preserving its “information content”. In this paper, a new method of feature space conversion is explored, wherein “enrichment” of the feature space is carried out by the augmentation of the existing features with new “implicit” features. The modus operandi involves generation of association rules in one case and closed frequent patterns in another and the extraction of the new features from these. This new feature space is first made use of independently to feed the classifier and then it is used in unison with the original feature space. The effectiveness of these methods is subsequently verified experimentally and expressed in terms of the classification accuracy achieved by the classifier.
Abhishek Srivastava, Osmar R. Zaïane, Maria-L
Added 03 Jun 2010
Updated 03 Jun 2010
Type Conference
Year 2007
Where IDEAS
Authors Abhishek Srivastava, Osmar R. Zaïane, Maria-Luiza Antonie
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