A theoretical study on six classifier fusion strategies

L.I. Kuncheva
2002 IEEE Transactions on Pattern Analysis and Machine Intelligence  
We look at a single point in the feature space, two classes, and L classifiers estimating the posterior probability for class ! 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.
doi:10.1109/34.982906 fatcat:4jaicjswjrfmlk3q4nitfwdc5a