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A Classifier Graph Based Recurring Concept Detection and Prediction Approach
Joint Authors
Sun, Yange
Wang, Zhihai
Bai, Yang
Dai, Honghua
Nahavandi, Saeid
Source
Computational Intelligence and Neuroscience
Issue
Vol. 2018, Issue 2018 (31 Dec. 2018), pp.1-13, 13 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2018-06-07
Country of Publication
Egypt
No. of Pages
13
Main Subjects
Abstract EN
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.
American Psychological Association (APA)
Sun, Yange& Wang, Zhihai& Bai, Yang& Dai, Honghua& Nahavandi, Saeid. 2018. A Classifier Graph Based Recurring Concept Detection and Prediction Approach. Computational Intelligence and Neuroscience،Vol. 2018, no. 2018, pp.1-13.
https://search.emarefa.net/detail/BIM-1130713
Modern Language Association (MLA)
Sun, Yange…[et al.]. A Classifier Graph Based Recurring Concept Detection and Prediction Approach. Computational Intelligence and Neuroscience No. 2018 (2018), pp.1-13.
https://search.emarefa.net/detail/BIM-1130713
American Medical Association (AMA)
Sun, Yange& Wang, Zhihai& Bai, Yang& Dai, Honghua& Nahavandi, Saeid. A Classifier Graph Based Recurring Concept Detection and Prediction Approach. Computational Intelligence and Neuroscience. 2018. Vol. 2018, no. 2018, pp.1-13.
https://search.emarefa.net/detail/BIM-1130713
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1130713