The Impact of Latency on Online Classification Learning with Concept Drift

  • Gary R. Marrs
  • Ray J. Hickey
  • Michaela M. Black
Conference paper

DOI: 10.1007/978-3-642-15280-1_42

Part of the Lecture Notes in Computer Science book series (LNCS, volume 6291)
Cite this paper as:
Marrs G.R., Hickey R.J., Black M.M. (2010) The Impact of Latency on Online Classification Learning with Concept Drift. In: Bi Y., Williams MA. (eds) Knowledge Science, Engineering and Management. KSEM 2010. Lecture Notes in Computer Science, vol 6291. Springer, Berlin, Heidelberg

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.

Keywords

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

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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Gary R. Marrs
    • 1
  • Ray J. Hickey
    • 1
  • Michaela M. Black
    • 1
  1. 1.School of Computing and EngineeringUniversity of Ulster, ColeraineCounty LondonderryN. Ireland

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