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.
CITATION STYLE
Togban, E., & Ziou, D. (2017). Classification using mixture of discriminative learners: The case of compositional data. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 10317 LNCS, pp. 416–425). Springer Verlag. https://doi.org/10.1007/978-3-319-59876-5_46
Mendeley helps you to discover research relevant for your work.