id: 05916046
dt: a
an: 05916046
au: Deckert, Magdalena
ti: Batch weighted ensemble for mining data streams with concept drift.
so: Kryszkiewicz, Marzena (ed.) et al., Foundations of intelligent systems.
    19th international symposium, ISMIS 2011, Warsaw, Poland, June 28‒30,
    2011. Proceedings. Berlin: Springer (ISBN 978-3-642-21915-3/pbk).
    Lecture Notes in Computer Science 6804. Lecture Notes in Artificial
    Intelligence, 290-299 (2011).
py: 2011
pu: Berlin: Springer
la: EN
cc: 
ut: 
ci: 
li: doi:10.1007/978-3-642-21916-0_32
ab: Summary: This paper presents a new framework for dealing with two main
    types of concept drift: sudden and gradual drift in labelled data with
    decision attribute. The learning examples are processed in batches of
    the same size. This new framework, called Batch Weighted Ensemble, is
    based on incorporating drift detector into the evolving ensemble. Its
    performance was evaluated experimentaly on data sets with different
    types of concept drift and compared with the performance of a standard
    Accuracy Weighted Ensemble classifier. The results show that BWE
    improves evaluation measures like processing time, memory used and
    obtain competitive total accuracy.
rv: