A decision theoretic approach to hierarchical classifier design

Abstract

The design of tree classifiers is considered from the statistical point of view. The procedure for calculating the a posteriori probabilities is decomposed into a sequence of steps. In every step the a posteriori probabilities for a certain subtask of the given pattern recognition task are calculated. The resulting tree classifier realizes a soft-decision strategy in contrast to the hard-decision strategy of the conventional decision tree. At the different nonterminal nodes, mean square polynomial classifiers are applied having the property of estimating the desired a posteriori probabilities together with an integrated feature selection capability.