Ensembles of Classifiers from Spatially Disjoint Data

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.