Please use this identifier to cite or link to this item:
PIRA download icon_1.1View/Download Full Text
Title: Bagging evolutionary feature extraction algorithm for classification
Authors: Zhao, T
Zhao, Q
Lu, H
Zhang, DD 
Issue Date: 2007
Source: ICNC 2007 : third International Conference on Natural Computation : Haikou, Hainan, China, 24-27 August, 2007 : proceedings, v. 3, p. 540-545
Abstract: Feature extraction is significant for pattern analysis and classification. Those based on genetic algorithms are promising owing to their potential parallelizability and possible applications in large scale and high dimensional data classification. Most recently, Zhao et al. presented a direct evolutionary feature extraction algorithm (DEFE) which can reduce the space complexity and improve the efficiency, thus overcoming the limitations of many genetic algorithm based feature extraction algorithms (EFE). However, DEFE does not consider the outlier problem which could deteriorate the classification performance, especially when the training sample set is small. Moreover, when there are many classes, the null space of within-class scatter matrix (S[sub w]) becomes small, resulting in poor discrimination performance in that space. In this paper, we propose a bagging evolutionary feature extraction algorithm (BEFE) incorporating bagging into a revised DEFE algorithm to improve the DEFE's performance in cases of small training sets and large number of classes. The proposed algorithm has been applied to face recognition and testified using the Yale and ORL face databases.
Keywords: Classification (of information)
Computational efficiency
Feature extraction
Problem solving
State space methods
Publisher: IEEE Computer Society
ISBN: 0-7695-2875-9
Rights: © 2007 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.
Appears in Collections:Conference Paper

Files in This Item:
File Description SizeFormat 
bagging-evolutionary_07.pdf259.39 kBAdobe PDFView/Open
Open Access Information
Status open access
File Version Version of Record
View full-text via PolyU eLinks SFX Query
Show full item record

Page views

Last Week
Last month
Citations as of Sep 24, 2023


Citations as of Sep 24, 2023


Last Week
Last month
Citations as of Sep 28, 2023

Google ScholarTM


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.