id: 05570987
dt: a
an: 05570987
au: Elwell, Ryan; Polikar, Robi
ti: Incremental learning of variable rate concept drift.
so: Benediktsson, Jón Atli (ed.) et al., Multiple classifier systems. 8th
    international workshop, MCS 2009, Reykjavik, Iceland, June 10‒12,
    2009. Proceedings. Berlin: Springer (ISBN 978-3-642-02325-5/pbk).
    Lecture Notes in Computer Science 5519, 142-151 (2009).
py: 2009
pu: Berlin: Springer
la: EN
cc: 
ut: nonstationary environment; concept drift; Learn$^{ + + }$.NSE
ci: 
li: doi:10.1007/978-3-642-02326-2_15
ab: Summary: We have recently introduced an incremental learning algorithm,
    Learn$^{ + + }$.NSE, for Non-Stationary Environments, where the data
    distribution changes over time due to concept drift. Learn$^{ + +
    }$.NSE is an ensemble of classifiers approach, training a new
    classifier on each consecutive batch of data that become available, and
    combining them through an age-adjusted dynamic error based weighted
    majority voting. Prior work has shown the algorithm’s ability to
    track gradually changing environments as well as its ability to retain
    former knowledge in cases of cyclical or recurring data by retaining
    and appropriately weighting all classifiers generated thus far. In this
    contribution, we extend the analysis of the algorithm to more
    challenging environments experiencing varying drift rates; but more
    importantly we present preliminary results on the ability of the
    algorithm to accommodate addition or subtraction of classes over time.
    Furthermore, we also present comparative results of a variation of the
    algorithm that employs an error-based pruning in cyclical environments.
rv: