Efficient and Effective Feature Selection in the Presence of Feature Interaction and Noise

Partridge, D., Wang, W. and Jones, P. (2001) Efficient and Effective Feature Selection in the Presence of Feature Interaction and Noise. In: Advances in Pattern Recognition — ICAPR 2001. Lecture Notes in Computer Science, 2013 . Springer Berlin / Heidelberg, BRA, pp. 196-201. ISBN 978-3-540-41767-5

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Abstract

This paper addresses the problem of feature subset selection for classification tasks. In particular, it focuses on the initial stages of complex realworld classification tasks when feature interaction is expected but illunderstood, and noise contaminating actual feature vectors must be expected to further complicate the classification problem. A neural-network based featureranking technique, the ‘clamping’ technique, is proposed as a robust and effective basis for feature selection that is more efficient than the established comparable techniques of sequential floating searches. The efficiency gain is that of an Order(n) algorithm over the Order(n2) floating search techniques. These claims are supported by an empirical study of a complex classification task.

Item Type: Book Section
Faculty \ School: Faculty of Science > School of Computing Sciences

University of East Anglia > Faculty of Science > Research Groups > Computational Biology (subgroups are shown below) > Machine learning in computational biology
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Depositing User: Vishal Gautam
Date Deposited: 25 Aug 2011 13:37
Last Modified: 30 Sep 2021 17:05
URI: https://ueaeprints.uea.ac.uk/id/eprint/21700
DOI: 10.1007/3-540-44732-6_20

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