Dempster-Shafer reasoning in large partially ordered sets: Applications in Machine Learning - Archive ouverte HAL Accéder directement au contenu
Chapitre D'ouvrage Année : 2010

Dempster-Shafer reasoning in large partially ordered sets: Applications in Machine Learning

Résumé

The Dempster-Shafer theory of belief functions has proved to be a powerful formalism for uncertain reasoning. However, belief functions on a finite frame of discernment Omega are usually defined in the power set 2^Omega, resulting in exponential complexity of the operations involved in this framework, such as combination rules. When Omega is linearly ordered, a usual trick is to work only with intervals, which drastically reduces the complexity of calculations. In this paper, we show that this trick can be extrapolated to frames endowed with an arbitrary lattice structure, not necessarily a linear order. This principle makes it possible to apply the Dempster-Shafer framework to very large frames such as, for instance, the power set of a finite set Omega, or the set of partitions of a finite set. Applications to multi-label classification and ensemble clustering are demonstrated.
Fichier principal
Vignette du fichier
ium2010_denoeux.pdf (158.48 Ko) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)
Loading...

Dates et versions

hal-00493015 , version 1 (17-06-2010)

Identifiants

Citer

Thierry Denœux, Marie-Hélène Masson. Dempster-Shafer reasoning in large partially ordered sets: Applications in Machine Learning. V.-N. Huyn, Y. Nakamori, J. Lawry and M. Inuigushi. Integrated Uncertainty Management and Applications, 68, Springer Berlin Heidelberg, pp.39-54, 2010, Advances in Intelligent and Soft Computing, ⟨10.1007/978-3-642-11960-6_5⟩. ⟨hal-00493015⟩
76 Consultations
149 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More