Combining binary classifiers with Imprecise probabilities

Destercke Sébastien, Quost Benjamin. 2011. Combining binary classifiers with Imprecise probabilities. In : Integrated uncertainty in knowledge modelling and decision making : International Symposium, IUKM 2011, Hangzhou, China, October 28-30, 2011. Proceedings. Tang Yongchuan (ed.), Huynh Van-Nam (ed.), Lawry Jonathan (ed.). Heidelberg : Springer [Allemagne], pp. 219-230. (Lecture Notes in Artificial Intelligence, 7027) ISBN 978-3-642-24917-4 International Symposium Integrated Uncertainty in Knowledge Modelling and Decision Making, Hangzhou, Chine, 28 October 2011/30 October 2011.

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Abstract : This paper proposes a simple framework to combine binary classifiers whose outputs are imprecise probabilities (or are transformed into some imprecise probabilities, e.g., by using confidence intervals). This combination comes down to solve linear programs describing constraints over events (here, subsets of classes). The number of constraints grows linearly with the number of classifiers, making the proposed framework tractable for problems involving a relatively large number of classes. After detailing the method, we provide some first experimental results illustrating the method interests. (Résumé d'auteur)

Mots-clés Agrovoc : Mathématique, Classification

Classification Agris : U10 - Computer science, mathematics and statistics

Auteurs et affiliations

  • Destercke Sébastien, CIRAD-PERSYST-UMR IATE (FRA)
  • Quost Benjamin, Université de Compiègne (FRA)

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