A theoretical study on six classifier fusion strategies

Abstract

We look at a single point in feature space, two classes, and L classifiers estimating the posterior probability for class /spl omega//sub 1/. Assuming that the estimates are independent and identically distributed (normal or uniform), we give formulas for the classification error for the following fusion methods: average, minimum, maximum, median, majority vote, and oracle.