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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Jing Gao Ding, B. Wei Fan Jiawei Han Yu, P.S. |
| Copyright Year | 1997 |
| Abstract | 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. |
| Sponsorship | IEEE Computer Society |
| File Size | 792348 |
| File Format | |
| ISSN | 10897801 |
| Volume Number | 12 |
| Issue Number | 6 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2008-11-01 |
| Publisher Place | U.S.A. |
| Access Restriction | One Nation One Subscription (ONOS) |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Predictive models Data analysis Classification algorithms Telecommunication traffic Traffic control Monitoring Delay Sampling methods Internet Current distribution skewed distributions data stream classification algorithms concept drifts data mining model averaging |
| Content Type | Text |
| Resource Type | Article |
| Subject | Computer Networks and Communications |
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