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Classifying Data Streams with Skewed Class Distributions and Concept Drifts

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5 Author(s)
Jing Gao ; Univ. of Illinois, Urbana, IL ; Bolin Ding ; Wei Fan ; Jiawei Han
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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.

Published in:

Internet Computing, IEEE  (Volume:12 ,  Issue: 6 )

Date of Publication:

Nov.-Dec. 2008

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