Houping Xiao, Yaliang Li, et al.
SDM 2015
Classification is an important data analysis tool that uses a model built from historical data to predict class labels for new observations. More and more applications are featuring data streams, rather than finite stored data sets, which are a challenge for traditional classification algorithms. Concept drifts and skewed distributions, two common properties of data stream applications, make the task of learning in streams difficult. The authors aim to develop a new approach to classify skewed data streams that uses an ensemble of models to match the distribution over under-samples of negatives and repeated samples of positives. © 2008 IEEE.
Houping Xiao, Yaliang Li, et al.
SDM 2015
Wei Fan, Haixun Wang, et al.
ICDCS 2002
Xufei Wang, Huan Liu, et al.
CIKM 2011
Kun-Lung Wu, Philip S. Yu, et al.
VLDB 2007