A classifier graph based recurring concept detection and prediction approach Sun, Yange, Wang, Zhihai, Bai, Yang, Dai, Honghua and Nahavandi, Saeid 2018, A classifier graph based recurring concept detection and prediction approach, Computational intelligence and neuroscience, vol. 2018, pp. 1-13, doi: 10.1155/2018/4276291. Attached Files Name Description MIMEType Size Downloads dai-classifiergraphbased-2018.pdf Published version application/pdf 1.81MB 60 Title A classifier graph based recurring concept detection and prediction approach Author(s) Sun, Yange Wang, Zhihai Bai, Yang Dai, Honghua orcid.org/0000-0001-9899-7029 Nahavandi, Saeid orcid.org/0000-0002-0360-5270 Journal name Computational intelligence and neuroscience Volume number 2018 Article ID 4276291 Start page 1 End page 13 Total pages 13 Publisher Hindawi Publishing Corporation Place of publication Cairo, Egypt Publication date 2018-06-07 ISSN 1687-5265 1687-5273 Keyword(s) recurring concepts data streams science & technology life sciences & biomedicine mathematical & computational biology neurosciences neurosciences & neurology Summary It is common in real-world data streams that previously seen concepts will reappear, which suggests a unique kind of concept drift, known as recurring concepts. Unfortunately, most of existing algorithms do not take full account of this case. Motivated by this challenge, a novel paradigm was proposed for capturing and exploiting recurring concepts in data streams. It not only incorporates a distribution-based change detector for handling concept drift but also captures recurring concept by storing recurring concepts in a classifier graph. The possibility of detecting recurring drifts allows reusing previously learnt models and enhancing the overall learning performance. Extensive experiments on both synthetic and real-world data streams reveal that the approach performs significantly better than the state-of-the-art algorithms, especially when concepts reappear. Language eng DOI 10.1155/2018/4276291 Field of Research 1109 Neurosciences 1702 Cognitive Science HERDC Research category C1 Refereed article in a scholarly journal ERA Research output type C Journal article Copyright notice ©2018, Yange Sun et al. Free to Read? Yes Use Rights Persistent URL http://hdl.handle.net/10536/DRO/DU:30111145 Document type: Journal Article Collections: Institute for Intelligent Systems Research and Innovation (IISRI) Open Access Collection GTP Research Related Links Link Description Connect to published version (open access) Go to link with your DU access privileges     Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved. Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au. Versions Version Filter Type Thu, 12 Jul 2018, 15:59:52 EST Fri, 13 Jul 2018, 05:04:54 EST Fri, 13 Jul 2018, 08:11:31 EST Wed, 18 Jul 2018, 10:00:25 EST Thu, 19 Jul 2018, 06:06:04 EST Thu, 19 Jul 2018, 12:34:06 EST Thu, 19 Jul 2018, 12:36:01 EST Thu, 19 Jul 2018, 12:36:49 EST Fri, 20 Jul 2018, 11:00:02 EST Fri, 07 Sep 2018, 18:35:12 EST Filtered Full Citation counts:   Cited 1 times in TR Web of Science Cited 4 times in Scopus Search Google Scholar Access Statistics: 322 Abstract Views, 62 File Downloads  -  Detailed Statistics Created: Wed, 18 Jul 2018, 10:00:25 EST

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