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A generic optimising feature extraction method using multiobjective genetic programming

Zhang, Y. and Rockett, P.I (2011) A generic optimising feature extraction method using multiobjective genetic programming. Applied Soft Computing, 11 (1). pp. 1087-1097. ISSN 1568-4946

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In this paper, we present a generic, optimising feature extraction method using multiobjective genetic programming. We re-examine the feature extraction problem and show that effective feature extraction can significantly enhance the performance of pattern recognition systems with simple classifiers. A framework is presented to evolve optimised feature extractors that transform an input pattern space into a decision space in which maximal class separability is obtained. We have applied this method to real world datasets from the UCI Machine Learning and StatLog databases to verify our approach and compare our proposed method with other reported results. We conclude that our algorithm is able to produce classifiers of superior (or equivalent) performance to the conventional classifiers examined, suggesting removal of the need to exhaustively evaluate a large family of conventional classifiers on any new problem. (C) 2010 Elsevier B.V. All rights reserved.

Item Type: Article
Copyright, Publisher and Additional Information: © 2011 Elsevier. This is an author produced version of a paper subsequently published in Applied Soft Computing. Uploaded in accordance with the publisher's self-archiving policy.
Keywords: Feature extraction; Multiobjective optimisation; Genetic programming; Pattern recognition
Institution: The University of Sheffield
Academic Units: The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Electronic and Electrical Engineering (Sheffield)
Depositing User: Miss Anthea Tucker
Date Deposited: 01 Oct 2010 13:10
Last Modified: 08 Feb 2013 17:07
Published Version: http://dx.doi.org/10.1016/j.asoc.2010.02.008
Status: Published
Publisher: Elsevier
Refereed: Yes
Identification Number: 10.1016/j.asoc.2010.02.008
URI: http://eprints.whiterose.ac.uk/id/eprint/11247

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