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. © Springer-Verlag Berlin Heidelberg 2005.
CITATION STYLE
Banfield, R. E., Hall, L. O., Bowyer, K. W., & Kegelmeyer, W. P. (2005). Ensembles of classifiers from spatially disjoint data. In Lecture Notes in Computer Science (Vol. 3541, pp. 196–205). Springer Verlag. https://doi.org/10.1007/11494683_20
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