The Impact of Latency on Online Classification Learning with Concept Drift
- 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
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.
KeywordsOnline Learning Classification Concept Drift Data stream Example life-cycle Latency ELISE CD3
Unable to display preview. Download preview PDF.