id: 05789284
dt: a
an: 05789284
au: Marrs, Gary R.; Hickey, Ray J.; Black, Michaela M.
ti: The impact of latency on online classification learning with concept drift.
so: Bi, Yaxin (ed.) et al., Knowledge science, engineering and management. 4th
    international conference, KSEM 2010, Belfast, Northern Ireland, UK,
    September 1‒3, 2010. Proceedings. Berlin: Springer (ISBN
    978-3-642-15279-5/pbk). Lecture Notes in Computer Science 6291. Lecture
    Notes in Artificial Intelligence, 459-469 (2010).
py: 2010
pu: Berlin: Springer
la: EN
cc: 
ut: online learning; classification; concept drift; data stream; example
    life-cycle; latency; ELISE; CD3
ci: 
li: doi:10.1007/978-3-642-15280-1_42
ab: Summary: 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.
rv: