Classification using mixture of discriminative learners: The case of compositional data

Abstract

Compositional data arise in many fields and their analysis has to be done with care since these data are bounded and summing up to a constant. In this paper, we propose a mixture model which combines several discriminative models through a set of Dirichlet-based weights. It is worth noticing that the Dirichlet distribution is not used here as a prior to the mixing coefficients but instead to model the repartition of the tasks among the classifiers. By doing so, we do not need to transform the data while keeping interpretable results. Experiments on synthetic and real-world data sets show the efficiency of our model.