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@INPROCEEDINGS{LNAI55900748,
     author = {Beyer, J{\"{o}}rg and Heesche, Kai and Hauptmann, Werner and Otte, Clemens and Kruse, Rudolf},
     editor = {Sossai, Claudio and Chemello, Gaetano},
      title = {Ensemble Learning for Multi-source Information Fusion},
  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/content/a1418720lx006841/},
        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.}
}