Multilevel classifiers based on a tree-structured set of Gaussian densities
This paper considers an approach to solving the problem of binary classification of objects. This approach is based on representing one of the classes by a sequence of Gaussian mixtures with further introduction of threshold decision rules. A method of constructing hierarchical sequences of Gaussian mixtures using the partial EM algorithm is proposed. We compare classifiers that use single Gaussian mixtures, cascades based on sequences of independent mixtures, cascades based on hierarchical sequences of mixtures, and classifiers that use trees of Gaussian densities for decision making. The theoretical estimates of computational costs for these classifiers are provided. The classifiers are tested on simulated data. The results are presented as the relations between the computational cost of classification and the obtained values of error criteria.