Distributed classifier migration in XCS for classification of electroencephalographic signals

Skinner, BT; Nguyen, HT; Liu, DK

Distributed classifier migration in XCS for classification of electroencephalographic signals

Skinner, BT

Nguyen, HT

Liu, DK

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Publication Type:: Conference Proceeding
Citation:: 2007 IEEE Congress on Evolutionary Computation, CEC 2007, 2007, pp. 2829 - 2836
Issue Date:: 2007-12-01

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Field	Value	Language
dc.contributor.author	Skinner, BT https://orcid.org/0000-0002-7869-4458	en_US
dc.contributor.author	Nguyen, HT https://orcid.org/0000-0003-3373-8178	en_US
dc.contributor.author	Liu, DK https://orcid.org/0000-0002-1581-5582	en_US
dc.date.issued	2007-12-01	en_US
dc.identifier.citation	2007 IEEE Congress on Evolutionary Computation, CEC 2007, 2007, pp. 2829 - 2836	en_US
dc.identifier.isbn	1424413400	en_US
dc.identifier.isbn	9781424413409	en_US
dc.identifier.uri	http://hdl.handle.net/10453/2615
dc.description.abstract	This paper presents an investigation into combining migration strategies inspired by multi-deme Parallel Genetic Algorithms with the XCS Learning Classifier System to provide parallel and distributed classifier migration. Migrations occur between distributed XCS classifier sub-populations using classifiers ranked according to numerosity, fitness or randomly selected. The influence of the degree-of-connectivity introduced by Fully-Connected, Bi-directional Ring and Uni-directional Ring topologies is examined. Results indicate that classifier migration is an effective method for improving classification accuracy, improving learning speed and reducing final classifier population size, in the single-step classification of noisy, artefact-inclusive human electroencephalographic signals. The experimental results will be used as part of our larger research effort investigating the feasibility of using EEG signals as an interface to allow paralysed persons to control a powered wheelchair or other devices. © 2007 IEEE.	en_US
dc.relation	http://purl.org/au-research/grants/arc/DP0666942
dc.relation	http://purl.org/au-research/grants/arc/LE0668541
dc.relation.ispartof	2007 IEEE Congress on Evolutionary Computation, CEC 2007	en_US
dc.relation.isbasedon	10.1109/CEC.2007.4424830	en_US
dc.title	Distributed classifier migration in XCS for classification of electroencephalographic signals	en_US
dc.type	Conference Proceeding
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
dc.location.activity	Singapore	en_US
pubs.embargo.period	Not known	en_US
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 Mechanical and Mechatronic Engineering
pubs.organisational-group	/University of Technology Sydney/Strength - CAS - Centre for Autonomous Systems
pubs.organisational-group	/University of Technology Sydney/Strength - CHT - Health Technologies
utslib.copyright.status	open_access
pubs.publication-status	Published	en_US

Abstract:

This paper presents an investigation into combining migration strategies inspired by multi-deme Parallel Genetic Algorithms with the XCS Learning Classifier System to provide parallel and distributed classifier migration. Migrations occur between distributed XCS classifier sub-populations using classifiers ranked according to numerosity, fitness or randomly selected. The influence of the degree-of-connectivity introduced by Fully-Connected, Bi-directional Ring and Uni-directional Ring topologies is examined. Results indicate that classifier migration is an effective method for improving classification accuracy, improving learning speed and reducing final classifier population size, in the single-step classification of noisy, artefact-inclusive human electroencephalographic signals. The experimental results will be used as part of our larger research effort investigating the feasibility of using EEG signals as an interface to allow paralysed persons to control a powered wheelchair or other devices. © 2007 IEEE.

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http://hdl.handle.net/10453/2615