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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Beyer, J., Heesche, K., Hauptmann, W., Otte, C., Kruse, R. (2009). Ensemble Learning for Multi-source Information Fusion. In: Sossai, C., Chemello, G. (eds) Symbolic and Quantitative Approaches to Reasoning with Uncertainty. ECSQARU 2009. Lecture Notes in Computer Science(), vol 5590. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02906-6_64
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DOI: https://doi.org/10.1007/978-3-642-02906-6_64
Publisher Name: Springer, Berlin, Heidelberg
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