Using classifier ensembles to label spatially disjoint data

We describe an ensemble approach to learning from arbitrarily partitioned data. The partitioning comes from the distributed processing requirements of a large scale simulation. The volume of the data is such that classifiers can train only on data local to a given partition. As a result of the partition reflecting the needs of the simulation, the class statistics can vary from partition to partition. Some classes will likely be missing from some partitions. We combine a fast ensemble learning algorithm with probabilistic majority voting in order to learn an accurate classifier from such data. Results from simulations of an impactor bar crushing a storage canister and from facial feature recognition show that regions of interest are successfully identified in spite of the class imbalance in the individual training sets.