Learning Coordination Classifiers
We present a new approach to ensemble classification that requires learning only a single base classifier. The idea is to learn a classifier that simultaneously predicts pairs of test labels--as opposed to learning multiple predictors for single test labels-- then coordinating the assignment of individual labels by propagating beliefs on a graph over the data. We argue that the approach is statistically well motivated, even for independent identically distributed (iid) data. In fact, we present experimental results that show improvements in classification accuracy over single-example classifiers, across a range of iid data sets and over a set of base classifiers. Like boosting, the technique increases representational capacity while controlling variance through a principled form of classifier combination.