Not Logged In

Feature Space Enrichment by Incorporation of Implicit Features for Effective Classification

Full Text: ideas07-p177.pdf PDF

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.

Citation

A. Srivastava, O. Zaiane, M. Antonie. "Feature Space Enrichment by Incorporation of Implicit Features for Effective Classification". International Database Engineering and Applications Symposium, Banff, Canada, pp 141-148, September 2007.

Keywords: Feature Selection, Classification
Category: In Conference

BibTeX

@incollection{Srivastava+al:IDEAS07,
  author = {Abhishek Srivastava and Osmar R. Zaiane and Maria-Luiza Antonie},
  title = {Feature Space Enrichment by Incorporation of Implicit Features for
    Effective Classification},
  Pages = {141-148},
  booktitle = { International Database Engineering and Applications Symposium},
  year = 2007,
}

Last Updated: February 04, 2020
Submitted by Sabina P

University of Alberta Logo AICML Logo