Abstract
In this chapter, we propose a new ensemble learning method. The main objective of this approach is to jointly use data-driven and knowledge-based submodels, like mathematical equations or rules, in the modeling process. The integration of knowledge-based submodels is of particular interest, since they are able to provide with 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. Using that kind of ensembles, the advantages of both models are combined, i.e., robustness and physical transparency of the knowledge-based models and approximation abilities of the data-driven learning. 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. (2010). Ensemble Learning for Multi-source Information Fusion. In: Pratihar, D.K., Jain, L.C. (eds) Intelligent Autonomous Systems. Studies in Computational Intelligence, vol 275. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-11676-6_6
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DOI: https://doi.org/10.1007/978-3-642-11676-6_6
Publisher Name: Springer, Berlin, Heidelberg
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