Abstract
The performance of neural nets can be improved through the use of ensembles of redundant nets. In this paper, some of the available methods of ensemble creation are reviewed and the “test and select” methodolology for ensemble creation is considered. This approach involves testing potential ensemble combinations on a validation set, and selecting the best performing ensemble on this basis, which is then tested on a final test set. The application of this methodology, and of ensembles in general, is explored further in two case studies. The first case study is of fault diagnosis in a diesel engine, and relies on ensembles of nets trained from three different data sources. The second case study is of robot localisation, using an evidence-shifting method based on the output of trained SOMs. In both studies, improved results are obtained as a result of combining nets to form ensembles.
We would like to thank the EPSRC Grant No.GR/K84257 for funding this research.
G.O.Chandroth is now at Lloyds Register, but his contribution to this paper was made whilst he was at University of Sheffield
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Sharkey, A.J.C., Sharkey, N.E., Gerecke, U., Chandroth, G.O. (2000). The “Test and Select” Approach to Ensemble Combination. In: Multiple Classifier Systems. MCS 2000. Lecture Notes in Computer Science, vol 1857. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45014-9_3
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DOI: https://doi.org/10.1007/3-540-45014-9_3
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