Knowledge-based extrapolation of cases: A possibilistic approach - Inria - Institut national de recherche en sciences et technologies du numérique Accéder directement au contenu
Chapitre D'ouvrage Année : 2002

Knowledge-based extrapolation of cases: A possibilistic approach

Résumé

The paper presents a formal framework of instance-based prediction in which the generalization beyond experience is founded on’the concepts of similarity and possibility. The underlying extrapolation principle is formalized by means of possibility rules, a special type of fuzzy rules. Thus, instance-based prediction can be realized as fuzzy set-based approximate reasoning. The basic model is extended by means of fuzzy set-based (linguistic) modeling techniques, including the discounting of untypical cases and the flexible handling and adequate adaptation of different similarity relations. This extension provides a convenient way of incorporating domain-specific (expert) knowledge. Our approach thus allows for combining knowledge and data in a flexible way and favors a view of instance-based reasoning according to which the user interacts closely with the system.

Dates et versions

hal-03378858 , version 1 (14-10-2021)

Identifiants

Citer

Eyke Hüllermeier, Didier Dubois, Henri Prade. Knowledge-based extrapolation of cases: A possibilistic approach. Bouchon-Meunier, Bennadette; Gutierrez-Rios, J.; Magdalena, L.; Yager, R.R. Technologies for Constructing Intelligent Systems 1 : Tools, 89, Physica-Verlag, pp.377-390, 2002, Studies in Fuzziness and Soft Computing book series (STUDFUZZ), 978-3790814545. ⟨10.1007/978-3-7908-1797-3_29⟩. ⟨hal-03378858⟩
12 Consultations
0 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More