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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.

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Title A classifier graph based recurring concept detection and prediction approach
Author(s) Sun, Yange
Wang, Zhihai
Bai, Yang
Dai, HonghuaORCID iD for Dai, Honghua orcid.org/0000-0001-9899-7029
Nahavandi, SaeidORCID iD for 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 Creative Commons Attribution licence
Persistent URL http://hdl.handle.net/10536/DRO/DU:30111145

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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.