Supervised learning rule selection for multiclass decision with performance constraints

A procedure to select a supervised rule for multiclass problem from a labeled dataset is proposed. The rule allows class-selective rejection and performance constraints. The unknown probabilities are estimated with a Parzen estimator. A set of rules are built by varying the Parzen¿s smoothness parameter of the marginal probabilities estimates and plugging them into the statistical hypothesis rules. A criterion that assesses the quality of these rules is estimated and used to select a rule. Resampling and aggregation methods are used to show the efficiency of the estimated criterion.