Ensemble Learning for Multi-source Information Fusion

Authors:
Jörg Beyer;Kai Heesche;Werner Hauptmann;Clemens Otte;Rudolf Kruse
Affiliations:
Siemens AG - Corporate Technology, Information and Communications,Learning Systems, Munich, Germany 80200 and School of Computer Science, Otto-von-Guericke-University Magdeburg, Magdeburg, Germany ...;Siemens AG - Corporate Technology, Information and Communications,Learning Systems, Munich, Germany 80200;Siemens AG - Corporate Technology, Information and Communications,Learning Systems, Munich, Germany 80200;Siemens AG - Corporate Technology, Information and Communications,Learning Systems, Munich, Germany 80200;School of Computer Science, Otto-von-Guericke-University Magdeburg, Magdeburg, Germany 39106
Venue:
ECSQARU '09 Proceedings of the 10th European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty
Year:
2009

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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.