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dc.contributor.authorGuo, Lihua
dc.contributor.authorLiew, Alan Wee-Chung
dc.contributor.editorLiew, AWC
dc.contributor.editorLovell, B
dc.contributor.editorFookes, C
dc.contributor.editorZhou, J
dc.contributor.editorGao, Y
dc.contributor.editorBlumenstein, M
dc.contributor.editorWang, Z
dc.date.accessioned2017-11-30T03:37:41Z
dc.date.available2017-11-30T03:37:41Z
dc.date.issued2016
dc.identifier.isbn9781509028962
dc.identifier.doi10.1109/DICTA.2016.7797069
dc.identifier.urihttp://hdl.handle.net/10072/124159
dc.description.abstractData stream mining has been receiving increased attention due to its presence in a wide range of applications, such as sensor networks, banking, and telecommunication. One of the most important challenges during learning from data streams is reacting to concept drift, i.e. unforeseen changes of the stream's underlying data distribution. Traditional methods always used online learning to handle the concept drift problem. However, online learning requires high time cost during online training. To overcome this shortcoming, this paper proposes a Kalman filtering approach, which can provide robust concept drift detection, to track concept drift. Once concept drift happens, the online extreme learning machine is applied to update the tracking model, whereas the offline extreme learning machine is used when no concept drift occurs. Based on this idea, we propose a fusion framework to combine online and offline extreme learning machine to efficiently track the data stream. The experiment results indicate the superior performance of our method.
dc.description.peerreviewedYes
dc.languageEnglish
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)
dc.publisher.placeAustralia
dc.relation.ispartofconferencenameInternational Conference on Digital Image Computing - Techniques and Applications (DICTA)
dc.relation.ispartofconferencetitle2016 INTERNATIONAL CONFERENCE ON DIGITAL IMAGE COMPUTING: TECHNIQUES AND APPLICATIONS (DICTA)
dc.relation.ispartofdatefrom2016-11-30
dc.relation.ispartofdateto2016-12-02
dc.relation.ispartoflocationGold Coast, AUSTRALIA
dc.relation.ispartofpagefrom65
dc.relation.ispartofpageto70
dc.subject.fieldofresearchPattern recognition
dc.subject.fieldofresearchData mining and knowledge discovery
dc.subject.fieldofresearchcode460308
dc.subject.fieldofresearchcode460502
dc.titleOnline-Offline Extreme Learning Machine with Concept Drift Tracking for Time Series Data
dc.typeConference output
dc.type.descriptionE1 - Conferences
dc.type.codeE - Conference Publications
gro.hasfulltextNo Full Text
gro.griffith.authorLiew, Alan Wee-Chung


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    Contains papers delivered by Griffith authors at national and international conferences.

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