Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/25177
Title: Parsimonious feature extraction based on genetic algorithms and support vector machines
Authors: Zhao, Q
Lu, H
Zhang, D 
Issue Date: 2006
Publisher: Springer
Source: Lecture notes in computer science (including subseries Lecture notes in artificial intelligence and lecture notes in bioinformatics), 2006, v. 3971 LNCS, p. 1387-1393 How to cite?
Journal: Lecture notes in computer science (including subseries Lecture notes in artificial intelligence and lecture notes in bioinformatics) 
Abstract: Most existing feature extraction algorithms aim at best preserving information in the original data or at improving the separability of data, but fail to consider the possibility of further reducing the number of used features. In this paper, we propose a parsimonious feature extraction algorithm. Its motivation is using as few features as possible to achieve the same or even better classification performance. It searches for the optimal features using a genetic algorithm and evaluates the features referring to Support Vector Machines. We tested the proposed algorithm by face recognition on the Yale and FERET databases. The experimental results proved its effectiveness and demonstrated that parsimoniousness should be a significant factor in developing efficient feature extraction algorithms.
Description: 3rd International Symposium on Neural Networks, ISNN 2006 - Advances in Neural Networks, Chengdu, 28 May -1 June 2006
URI: http://hdl.handle.net/10397/25177
ISBN: 354034439X
9783540344391
ISSN: 0302-9743
EISSN: 1611-3349
DOI: 10.1007/11759966_206
Appears in Collections:Conference Paper

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