Abstract
We describe an ensemble learning approach that accurately learns from data that has been partitioned according to the arbitrary spatial requirements of a large-scale simulation wherein classifiers may be trained only on the data local to a given partition. As a result, the class statistics can vary from partition to partition; some classes may even be missing from some partitions. In order to learn from such data, we combine a fast ensemble learning algorithm with Bayesian decision theory to generate an accurate predictive model of the simulation data. Results from a simulation of an impactor bar crushing a storage canister and from region recognition in face images show that regions of interest are successfully identified.
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© 2005 Springer-Verlag Berlin Heidelberg
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Banfield, R.E., Hall, L.O., Bowyer, K.W., Kegelmeyer, W.P. (2005). Ensembles of Classifiers from Spatially Disjoint Data. In: Oza, N.C., Polikar, R., Kittler, J., Roli, F. (eds) Multiple Classifier Systems. MCS 2005. Lecture Notes in Computer Science, vol 3541. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11494683_20
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DOI: https://doi.org/10.1007/11494683_20
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-26306-7
Online ISBN: 978-3-540-31578-0
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