Use of Bayesian data reduction for the fusion of legacy classifiers

Abstract For important classification tasks there may already be extant an arsenal of classification tools, these representing previous attempts and best efforts at a solution. Many times these are useful classifiers; and although the fact that all base their decisions on the same observations implies that their decisions are strongly dependent in a way that is difficult to model, there is often some benefit from fusing them to a better corporate decision. One can consider this fusion as of building a meta-classifier, based on data vectors whose elements are the individual legacy classifier (LC) decisions. The Bayesian data reduction algorithm imposes a uniform prior probability mass function on discrete symbol probabilities. It was developed previously, and in this paper is applied to the preceding decision-fusion problem, with favorable comparison to a number of other expert-mixing approaches. Parameters varied include the number of relevant LCs (some may have been poorly designed, and ought to be discounted/discarded automatically), the numbers of training data and classes, and the dependence between LCs––a fusion approach should reject redundant decisions.