A modified Learn++.NSE algorithm for dealing with concept drift

Dong, F; Lu, J; Zhang, G; Li, K

A modified Learn++.NSE algorithm for dealing with concept drift

Dong, F Lu, J

Zhang, G

Li, K

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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. 556 - 561
Issue Date:: 2014-01-01

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Field	Value	Language
dc.contributor.author	Dong, F	en_US
dc.contributor.author	Lu, J https://orcid.org/0000-0003-0690-4732	en_US
dc.contributor.author	Zhang, G https://orcid.org/0000-0003-3960-0583	en_US
dc.contributor.author	Li, K	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. 556 - 561	en_US
dc.identifier.isbn	9789814619967	en_US
dc.identifier.uri	http://hdl.handle.net/10453/33872
dc.description.abstract	© 2014 by World Scientific Publishing Co. Pte. Ltd. All rights reserved. Concept drift is a very pervasive phenomenon in real world applications. By virtue of variety change types of concept drift, it makes more difficult for learning algorithm to track the concept drift very closely. Learn++.NSE is an incremental ensemble learner without any assumption on change type of concept drift. Even though it has good performance on handling concept drift, but it costs high computation and needs more time to recover from accuracy drop. This paper proposed a modified Learn++.NSE algorithm. During learning instances in data stream, our algorithm first identifies where and when drift happened, then uses instances accumulated by drift detection method to create a new base classifier, and finally organized all existing classifiers based on Learn++.NSE weighting mechanism to update ensemble learner. This modified algorithm can reduce high computation cost without any performance drop and improve the accuracy recover speed when drift happened.	en_US
dc.relation.ispartof	Decision Making and Soft Computing - Proceedings of the 11th International FLINS Conference, FLINS 2014	en_US
dc.title	A modified Learn++.NSE algorithm for dealing with concept drift	en_US
dc.type	Conference Proceeding
utslib.for	080105 Expert Systems	en_US
utslib.for	080108 Neural, Evolutionary and Fuzzy Computation	en_US
utslib.for	080605 Decision Support and Group Support Systems	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	open_access
pubs.publication-status	Published	en_US

Abstract:

© 2014 by World Scientific Publishing Co. Pte. Ltd. All rights reserved. Concept drift is a very pervasive phenomenon in real world applications. By virtue of variety change types of concept drift, it makes more difficult for learning algorithm to track the concept drift very closely. Learn++.NSE is an incremental ensemble learner without any assumption on change type of concept drift. Even though it has good performance on handling concept drift, but it costs high computation and needs more time to recover from accuracy drop. This paper proposed a modified Learn++.NSE algorithm. During learning instances in data stream, our algorithm first identifies where and when drift happened, then uses instances accumulated by drift detection method to create a new base classifier, and finally organized all existing classifiers based on Learn++.NSE weighting mechanism to update ensemble learner. This modified algorithm can reduce high computation cost without any performance drop and improve the accuracy recover speed when drift happened.

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http://hdl.handle.net/10453/33872