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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Ditzler, G. Polikar, R. |
| Copyright Year | 2011 |
| Description | Author affiliation: Signal Processing and Pattern Recognition Laboratory in the Electrical & Computer Engineering, Department at Rowan University located in Glassboro, NJ, 08028, USA (Ditzler, G.; Polikar, R.) |
| Abstract | Learning in nonstationary environments, also called learning concept drift, has been receiving increasing attention due to increasingly large number of applications that generate data with drifting distributions. These applications are usually associated with streaming data, either online or in batches, and concept drift algorithms are trained to detect and track the drifting concepts. While concept drift itself is a significantly more complex problem than the traditional machine learning paradigm of data coming from a fixed distribution, the problem is further complicated when obtaining labeled data is expensive, and training must rely, in part, on unlabelled data. Independently from concept drift research, semi-supervised approaches have been developed for learning from (limited) labeled and (abundant) unlabeled data; however, such approaches have been largely absent in concept drift literature. In this contribution, we describe an ensemble of classifiers based approach that takes advantage of both labeled and unlabeled data in addressing concept drift: available labeled data are used to generate classifiers, whose voting weights are determined based on the distances between Gaussian mixture model components trained on both labeled and unlabeled data in a drifting environment. |
| Starting Page | 2741 |
| Ending Page | 2748 |
| File Size | 823624 |
| Page Count | 8 |
| File Format | |
| ISBN | 9781424496358 |
| ISSN | 21614407 |
| e-ISBN | 9781424496372 |
| DOI | 10.1109/IJCNN.2011.6033578 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2011-07-31 |
| Publisher Place | USA |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Classification algorithms Data models Clustering algorithms Training data Training Testing Algorithm design and analysis incremental learning concept drift non-stationary environments unlabeled data ensemble systems |
| Content Type | Text |
| Resource Type | Article |
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