The Impact of Latency on Online Classification Learning with Concept Drift

Gary R. Marrs, RJ Hickey, Michaela Black

Research output: Chapter in Book/Report/Conference proceedingConference contribution

22 Citations (Scopus)
13 Downloads (Pure)

Abstract

Online classification learners operating under concept drift can be subject to latency in examples arriving at the training base. A discussion of latency and the related notion of example filtering leads to the development of an example life cycle for online learning (OLLC). Latency in a data stream is modelled in a new Example Life-cycle Integrated Simulation Environment (ELISE). In a series of experiments, the online learner algorithm CD3 is evaluated under several drift and latency scenarios. Results show that systems subject to large random latencies can, when drift occurs, suffer substantial deterioration in classification rate with slow recovery.
Original languageEnglish
Title of host publicationUnknown Host Publication
PublisherSpringer
Pages459-469
Number of pages10
Volume6291/2
DOIs
Publication statusPublished - 2010
EventThe fourth International Conference on Knowledge Science, Engineering and Management (KSEM`2010) - Belfast, Northern Ireland
Duration: 1 Jan 2010 → …

Conference

ConferenceThe fourth International Conference on Knowledge Science, Engineering and Management (KSEM`2010)
Period1/01/10 → …

Keywords

  • Online Learning
  • Classification
  • Concept Drift
  • Data stream
  • Example life-cycle
  • Latency
  • ELISE
  • CD3

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  • Cite this

    Marrs, G. R., Hickey, RJ., & Black, M. (2010). The Impact of Latency on Online Classification Learning with Concept Drift. In Unknown Host Publication (Vol. 6291/2, pp. 459-469). Springer. https://doi.org/10.1007/978-3-642-15280-1_42