Case-Base Maintenance for Concept Drift

Lu, P; Lu, J; Zhang, G

Case-Base Maintenance for Concept Drift

Lu, P Lu, J

Zhang, G

Permalink

Publisher:: WASET
Publication Type:: Conference Proceeding
Citation:: WASET Issue 72: Proceedings of the International Conference on Information Systems, 2012, pp. 333 - 340
Issue Date:: 2012-01

Closed Access

	Filename	Description	Size
	2012001099OK.pdf		3.03 MB	Adobe PDF	View/Open

Copyright Clearance Process

Recently Added
In Progress
Closed Access

This item is closed access and not available.

Full metadata record

Field	Value	Language
dc.contributor.author	Lu, P	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.editor	WASET	en_US
dc.date	2012-08-30	en_US
dc.date.issued	2012-01	en_US
dc.identifier.citation	WASET Issue 72: Proceedings of the International Conference on Information Systems, 2012, pp. 333 - 340	en_US
dc.identifier.issn	2010-376X	en_US
dc.identifier.uri	http://hdl.handle.net/10453/22983
dc.description.abstract	As the evolving nature of a real data stream, so called concept drift, and its accumulating volume, any deployed Case-Based Reasoning (CBR) system will nead to have procedures for Case-Base Maintenance (CBM). Traditional CBM methods for handling concept drift cannot well distinguish between noise and true concept drift, some of them may even run under the risk of losing case-base competence. Motivated by these two problems, wc present a twostage CBM approach. We propose a Noise-Enhanced Fast Context Switch (NEFCS) algorithm, which targets to remove noise under dynamic environment as Stage 1. We also propose a Recursive Conservative Redundancy Removal (RCRR) algorithm, which removes redundant cases in a recursively unifonn way while keeping the case·base coveragc as Stage 2. Experimental evaluations on realworld datasels show that our approach significant improves the perfonnance compared with other CBM approaches.	en_US
dc.publisher	WASET	en_US
dc.relation	http://purl.org/au-research/grants/arc/DP110103733
dc.relation.ispartof	WASET Issue 72: Proceedings of the International Conference on Information Systems	en_US
dc.relation.ispartof	International Conference on Information Systems	en_US
dc.title	Case-Base Maintenance for Concept Drift	en_US
dc.type	Conference Proceeding
utslib.location	Penang, Malaysia	en_US
utslib.location.activity	Penang, Malaysia	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
dc.location.activity	Penang, Malaysia	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 Computer Science
pubs.organisational-group	/University of Technology Sydney/Strength - CAI - Centre for Artificial Intelligence
utslib.copyright.status	closed_access
pubs.consider-herdc	true	en_US
pubs.finish-date	2012-09-02	en_US
pubs.place-of-publication	Penang, Malaysia	en_US
pubs.start-date	2012-08-30	en_US

Abstract:

As the evolving nature of a real data stream, so called concept drift, and its accumulating volume, any deployed Case-Based Reasoning (CBR) system will nead to have procedures for Case-Base Maintenance (CBM). Traditional CBM methods for handling concept drift cannot well distinguish between noise and true concept drift, some of them may even run under the risk of losing case-base competence. Motivated by these two problems, wc present a twostage CBM approach. We propose a Noise-Enhanced Fast Context Switch (NEFCS) algorithm, which targets to remove noise under dynamic environment as Stage 1. We also propose a Recursive Conservative Redundancy Removal (RCRR) algorithm, which removes redundant cases in a recursively unifonn way while keeping the case·base coveragc as Stage 2. Experimental evaluations on realworld datasels show that our approach significant improves the perfonnance compared with other CBM approaches.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/22983