Confusion-based fusion of classifiers

Given a finite collection of classifiers trained on two-class data one wishes to fuse the classifiers to form a new classifier with improved performance. Typically, the fusion is done at the output level using logical ANDs and ORs. The proposed fusion is based on the location of the feature vector with respect to the expertise sets and confusion sets of the classifiers. Given a feature vector x, if any one of the classifiers is an expert on x then the fusion rule should be an OR. If the classifiers are confused at x then the fusion rule should be defined is such a way to reflect this confusion or uncertainty. We give this fusion rule that is based upon the confusion sets as well as the expertise sets. We believe that this fusion rule will produce classifiers that perform better than classifiers that resulted from other fusion rules.