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Supervised learning rule selection for multiclass decision with performance constraints

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Abstract

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.
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Dates and versions

hal-02362961 , version 1 (14-11-2019)

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Nisrine Jrad, Edith Grall-Maës, Pierre Beauseroy. Supervised learning rule selection for multiclass decision with performance constraints. ICPR 2008 19th International Conference on Pattern Recognition, Dec 2008, Tampa, United States. pp.1-4, ⟨10.1109/ICPR.2008.4761200⟩. ⟨hal-02362961⟩
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