Heterogeneous cross domain ranking in latent space
Bo Wang, Jie Tang, et al.
CIKM 2009
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.
Bo Wang, Jie Tang, et al.
CIKM 2009
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KDD 2003
Jing Gao, Wei Fan, et al.
ICDM 2011
Jing Gao, Feng Liang, et al.
NeurIPS 2009