Gaussian Mixture Models for multiclass problems with performance constraints
Nisrine Jrad, Edith Grall‐Maës, Pierre Beauseroy · 2009
Abstract. This paper proposes a method using labelled data to learn a decision rule for multiclass problems with class-selective rejection and performance constraints. The method is based on class-conditional density estimations obtained by using the Gaussian Mixture Models (GMM). The rule is thus determined by plugging these estimations in the statistical hypothesis framework and solving an optimization problem. Two simulations are then carried out to corroborate the efficiency of the proposed method. Experimental results show that it compares well with a non-parametric solution using Parzen estimator.