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  •   Open Research
  • AUT Faculties
  • Faculty of Design and Creative Technologies (Te Ara Auaha)
  • School of Engineering, Computer and Mathematical Sciences - Te Kura Mātai Pūhanga, Rorohiko, Pāngarau
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One Pass Concept Change Detection for Data Streams

Sakthithasan, S; Pears, RL; Koh, YS
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http://hdl.handle.net/10292/5298
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Abstract
In this research we present a novel approach to the concept change detection problem. Change detection is a fundamental issue with data stream mining as models generated need to be updated when significant changes in the underlying data distribution occur. A number of change detection approaches have been proposed but they all suffer from limitations such as high computational complexity, poor sensitivity to gradual change, or the opposite problem of high false positive rate. Our approach, termed OnePassSampler, has low computational complexity as it avoids multiple scans on its memory buffer by sequentially processing data. Extensive experimentation on a wide variety of datasets reveals that OnePassSampler has a smaller false detection rate and smaller computational overheads while maintaining a competitive true detection rate to ADWIN2.
Keywords
Data stream mining; Concept drift detection; Bernstein bound
Date
April 2013
Source
Advances in Knowledge Discovery and Data Mining. Lecture Notes in Computer Science Volume 7819, 2013, pp 461-472.
Item Type
Conference Contribution
Publisher
Springer Verlag
DOI
10.1007/978-3-642-37456-2_39
Rights Statement
The author may only post his/her version provided acknowledgement is given to the original source of publication and a link is inserted to the published article on Springer's website. The link must be accompanied by the following text: "The final publication is available at www.springerlink.com”. (Please also see Publisher’s Version and Citation)

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