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Database Engineering and Applications Symposium, International (2007)
Banff, Alberta, Canada
Sept. 6, 2007 to Sept. 8, 2007
ISSN: 1098-8068
ISBN: 0-7695-2947-X
pp: 141-148
Osmar R. Zaiane , University of Alberta, Canada
Abhishek Srivastava , University of Alberta, Canada
Maria-Luiza Antonie , University of Alberta, Canada
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. <p>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.</p>
Osmar R. Zaiane, Abhishek Srivastava, Maria-Luiza Antonie, "Feature Space Enrichment by Incorporation of Implicit Features for Effective Classification", Database Engineering and Applications Symposium, International, vol. 00, no. , pp. 141-148, 2007, doi:10.1109/IDEAS.2007.28
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