A novel weighting method for online ensemble learning with the presence of concept drift

Liu, A; Zhang, G; Lu, J

A novel weighting method for online ensemble learning with the presence of concept drift

Liu, A

Zhang, G

Lu, J

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Publication Type:: Conference Proceeding
Citation:: Decision Making and Soft Computing - Proceedings of the 11th International FLINS Conference, FLINS 2014, 2014, pp. 550 - 555
Issue Date:: 2014-01-01

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Field	Value	Language
dc.contributor.author	Liu, A https://orcid.org/0000-0002-0733-7138	en_US
dc.contributor.author	Zhang, G https://orcid.org/0000-0003-3960-0583	en_US
dc.contributor.author	Lu, J https://orcid.org/0000-0003-0690-4732	en_US
dc.date.issued	2014-01-01	en_US
dc.identifier.citation	Decision Making and Soft Computing - Proceedings of the 11th International FLINS Conference, FLINS 2014, 2014, pp. 550 - 555	en_US
dc.identifier.isbn	9789814619967	en_US
dc.identifier.uri	http://hdl.handle.net/10453/35646
dc.description.abstract	© 2014 by World Scientific Publishing Co. Pte. Ltd. All rights reserved. Ensemble of classifiers is a very popular method for online and incremental learning in non-stationary environment, as it improves the accuracy of single classifiers and is able to recover from drifting concept without explicit drift detection. However, current ensemble weighing methods do not consider the relationship between a test instance and each ensemble member's training domain. As a result, a locally correct ensemble member may be reduced weight unfairly because that its prediction result of an out of domain test instance is wrong. These inaccuracies will increases when there is a significant concept change. In this paper, therefore, we proposed a fuzzy online ensemble weighting method which takes the consideration of the degree of membership of each instance in each ensemble member and a modified majority voting method to improve the ability of ensembles on handling online classification tasks with concept drift.	en_US
dc.relation.ispartof	Decision Making and Soft Computing - Proceedings of the 11th International FLINS Conference, FLINS 2014	en_US
dc.title	A novel weighting method for online ensemble learning with the presence of concept drift	en_US
dc.type	Conference Proceeding
utslib.for	080105 Expert Systems	en_US
utslib.for	080605 Decision Support and Group Support Systems	en_US
utslib.for	080108 Neural, Evolutionary and Fuzzy Computation	en_US
dc.location.activity	Brazil
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 Computer Science
pubs.organisational-group	/University of Technology Sydney/Strength - CAI - Centre for Artificial Intelligence
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US

Abstract:

© 2014 by World Scientific Publishing Co. Pte. Ltd. All rights reserved. Ensemble of classifiers is a very popular method for online and incremental learning in non-stationary environment, as it improves the accuracy of single classifiers and is able to recover from drifting concept without explicit drift detection. However, current ensemble weighing methods do not consider the relationship between a test instance and each ensemble member's training domain. As a result, a locally correct ensemble member may be reduced weight unfairly because that its prediction result of an out of domain test instance is wrong. These inaccuracies will increases when there is a significant concept change. In this paper, therefore, we proposed a fuzzy online ensemble weighting method which takes the consideration of the degree of membership of each instance in each ensemble member and a modified majority voting method to improve the ability of ensembles on handling online classification tasks with concept drift.

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http://hdl.handle.net/10453/35646