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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Breve, F. Liang Zhao |
| Copyright Year | 2012 |
| Description | Author affiliation: University of São Paulo (USP), São Carlos, Brazil (Liang Zhao) || São Paulo State University (UNESP), Rio Claro, Brazil (Breve, F.) |
| Abstract | Concept drift is a problem of increasing importance in machine learning and data mining. Data sets under analysis are no longer only static databases, but also data streams in which concepts and data distributions may not be stable over time. However, most learning algorithms produced so far are based on the assumption that data comes from a fixed distribution, so they are not suitable to handle concept drifts. Moreover, some concept drifts applications requires fast response, which means an algorithm must always be (re)trained with the latest available data. But the process of labeling data is usually expensive and/or time consuming when compared to unlabeled data acquisition, thus only a small fraction of the incoming data may be effectively labeled. Semi-supervised learning methods may help in this scenario, as they use both labeled and unlabeled data in the training process. However, most of them are also based on the assumption that the data is static. Therefore, semi-supervised learning with concept drifts is still an open challenge in machine learning. Recently, a particle competition and cooperation approach was used to realize graph-based semi-supervised learning from static data. In this paper, we extend that approach to handle data streams and concept drift. The result is a passive algorithm using a single classifier, which naturally adapts to concept changes, without any explicit drift detection mechanism. Its built-in mechanisms provide a natural way of learning from new data, gradually “forgetting” older knowledge as older labeled data items became less influent on the classification of newer data items. Some computer simulation are presented, showing the effectiveness of the proposed method. |
| Starting Page | 1 |
| Ending Page | 6 |
| File Size | 805112 |
| Page Count | 6 |
| File Format | |
| ISBN | 9781467314886 |
| ISSN | 21614393 |
| e-ISBN | 9781467314909 |
| e-ISBN | 9781467314893 |
| DOI | 10.1109/IJCNN.2012.6252617 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2012-06-10 |
| Publisher Place | Australia |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Machine learning Computer simulation Vectors Educational institutions Electronic mail Machine learning algorithms Algorithm design and analysis Concept Drift Semi-Supervised Learning Particle Competition and Cooperation |
| Content Type | Text |
| Resource Type | Article |
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