Fast graph pattern matching
Jiefeng Cheng, Jeffrey Xu Yu, et al.
ICDE 2008
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.
Jiefeng Cheng, Jeffrey Xu Yu, et al.
ICDE 2008
Haixun Wang, Fang Chu, et al.
SSDBM 2004
Wei Fan, Haixun Wang, et al.
ICDM 2002
Houping Xiao, Jing Gao, et al.
WWW 2015