Amidst the conflicting experimental evidence of superiority of one over the other, we investigate the Sum and majority Vote combining rules in a two class case, under the assumption of experts being of equal strength and estimation errors conditionally independent and identically distributed. We show, analytically, that, for Gaussian estimation error distributions, Sum always outperforms Vote. For heavy tail distributions, we demonstrate by simulation that Vote may outperform Sum. Results on synthetic data confirm the theoretical predictions. Experiments on real data support the general findings, but also show the effect of the usual assumptions of conditional independence, identical error distributions, and common target outputs of the experts not being fully satisfied.

Among existing classifier combination rules the most widely used are sum, product and vote. Although product is more directly related to the compound class posterior probability, it does not perform well. Sum, which is derived under restricting assumptions, outperforms product, especially if the class aposteriori probability estimates are subject to high levels of noise. We establish the cause of product's degraded performance and propose a method to improve it. Tests on real and synthetic data demonstrate that the modified product has a number of advantages in relation to other rules that we experiment with.