TY  - CONF
ID  - LNAI55900748
T1  - Ensemble Learning for Multi-source Information Fusion
A1  - Beyer, Jörg
A1  - Heesche, Kai
A1  - Hauptmann, Werner
A1  - Otte, Clemens
A1  - Kruse, Rudolf
ED  - Sossai, Claudio
ED  - Chemello, Gaetano
TI  - Symbolic and Quantitative Approaches to Reasoning with Uncertainty
T3  - Lecture Notes in Computer Science
Y1  - 2009
VL  - 5590
SP  - 748
EP  - 756
PB  - Springer
CY  - Heidelberg
SN  - 978-3-642-02905-9
SN  - 0302-9743
UR  - http://www.springerlink.com/content/a1418720lx006841/
M2  - doi: 10.1007/978-3-642-02906-6_64
N2  - 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.
ER  -