A Combined Ant Colony and Differential Evolution Feature Selection Algorithm

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dc.contributor.author Khushaba, RN
dc.contributor.author Al-Ani, A
dc.contributor.author Alsukker, AS
dc.contributor.author Al-Jumaily, A
dc.contributor.editor Dorigo, M
dc.contributor.editor Birattari, M
dc.contributor.editor Blum, C
dc.contributor.editor Clerc, M
dc.contributor.editor Stà tzle, T
dc.contributor.editor Winfield, A
dc.date.accessioned 2010-05-28T09:38:08Z
dc.date.issued 2008-01
dc.identifier.citation Lecture Notes In Computer Science Vol 5217: Ant Colony Optimization and Swarm Intelligence, 2008, pp. 1 - 12
dc.identifier.isbn 978-3-540-87526-0
dc.identifier.other E1 en_US
dc.identifier.uri http://hdl.handle.net/10453/7841
dc.description.abstract Feature selection is an important step in many pattern recognition systems that aims to overcome the so-called curse of dimensionality problem. Although Ant Colony Optimization (ACO) proved to be a powerful technique in different optimization problems, but it still needs some improvements when applied to the feature selection problem. This is due to the fact that it builds its solutions sequentially, where in feature selection this behavior will most likely not lead to the optimal solution. In this paper, a novel feature selection algorithm based on a combination of ACO and a simple, yet powerful, Differential Evolution (DE) operator is presented. The proposed combination enhances both the exploration and exploitation capabilities of the search procedure. The new algorithm is tested on two biosignal-driven applications. The performance of the proposed algorithm is compared with other dimensionality reduction techniques to prove its superiority.
dc.publisher Springer
dc.relation.hasversion Accepted manuscript version en_US
dc.relation.isbasedon 10.1007/978-3-540-87527-7_1
dc.subject NA
dc.subject NA
dc.title A Combined Ant Colony and Differential Evolution Feature Selection Algorithm
dc.type Conference Proceeding
dc.parent Lecture Notes In Computer Science Vol 5217: Ant Colony Optimization and Swarm Intelligence
dc.journal.number en_US
dc.publocation Belgium en_US
dc.publocation Belgium
dc.identifier.startpage 1 en_US
dc.identifier.endpage 12 en_US
dc.cauo.name FEIT.School of Elec, Mech and Mechatronic Systems en_US
dc.conference Verified OK en_US
dc.conference International Workshop on Ant Colony
dc.for 080109 Pattern Recognition and Data Mining
dc.personcode 101188 en_US
dc.personcode 040052 en_US
dc.personcode 10091395 en_US
dc.personcode 011083 en_US
dc.percentage 100 en_US
dc.classification.name Pattern Recognition and Data Mining en_US
dc.classification.type FOR-08 en_US
dc.edition 1 en_US
dc.custom en_US
dc.custom International Workshop on Ant Colony en_US
dc.date.activity en_US
dc.date.activity 20080922 en_US
dc.date.activity 2008-09-22
dc.location.activity en_US
dc.location.activity Brussels, Belgium en_US
dc.location.activity Brussels, Belgium
dc.description.keywords NA en_US
dc.description.keywords NA
dc.staffid en_US
dc.staffid 011083 en_US
pubs.embargo.period Not known
pubs.organisational-group /University of Technology Sydney
pubs.organisational-group /University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group /University of Technology Sydney/Faculty of Engineering and Information Technology/School of Elec, Mech and Mechatronic Systems
pubs.organisational-group /University of Technology Sydney/Strength - Health Technologies


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