The design of an optimal Bayesian classifier for multiple features is dependent on the estimation of multidimensional joint probability density functions and therefore requires a design sample size that increases exponentially with the number of dimensions. A method was developed that combines classification decisions from marginal density functions using an additional classifier. Unlike voting methods, this method can select a more appropriate class than the ones selected by the marginal classifiers, thus "overriding" their decisions. It is shown that this method always exhibits an asymptotic probability of error no worse than the probability of error of the best marginal classifier.
['Mark D. Happel', 'Peter Bock']
OVERRIDING THE EXPERTS: A FUSION METHOD FOR COMBINING MARGINAL CLASSIFIERS