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Ensemble Learning for Multi-source Information Fusion
| Type of publication: | Inproceedings |
| Citation: | LNAI55900748 |
| Booktitle: | Symbolic and Quantitative Approaches to Reasoning with Uncertainty |
| Series: | Lecture Notes in Computer Science |
| Volume: | 5590 |
| Year: | 2009 |
| Pages: | 748--756 |
| Publisher: | Springer |
| Location: | Heidelberg |
| ISSN: | 0302-9743 |
| ISBN: | 978-3-642-02905-9 |
| URL: | http://www.springerlink.com/co... |
| DOI: | 10.1007/978-3-642-02906-6_64 |
| Abstract: | In this paper, a new ensemble learning method is proposed. The main objective of this approach is to jointly use knowledge-based and data-driven submodels in the modeling process. The integration of knowledge-based submodels is of particular interest, since they are able to provide information not contained in the data. On the other hand, data-driven models can complement the knowledge-based models with respect to input space coverage. For the task of appropriately integrating the different models, a method for partitioning the input space for the given models is introduced. The benefits of this approach are demonstrated for a real-world application. |
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| Added by: | [GR] |
| Total mark: | 0 |
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