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
Jing Gao, Wei Fan, et al.
KDD 2008
Haixun Wang, Wei Fan, et al.
KDD 2003
Haixun Wang, Sanghyun Park, et al.
SIGMOD 2003