Classifying Data Streams with Skewed Class Distributions and Concept Drifts

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.

In  IEEE Internet Computing

Publisher  IEEE Computer Society

Details

TypeArticle
URLhttp://www.computer.org/portal/web/csdl/doi/10.1109/MIC.2008.119
Pages37-49
Volume12
Number6
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