Imprecise modelling using gradual rules and its application to the classification of time series

Authors:
Sylvie Galichet;Didier Dubois;Henri Prade
Affiliations:
Institut de Recherche en Informatique de Toulouse, UniversitØ Paul Sabatier, Toulouse Cedex and LISTIC, UniversitØ de Savoie, Annecy Cedex;Institut de Recherche en Informatique de Toulouse, UniversitØ Paul Sabatier, Toulouse Cedex;Institut de Recherche en Informatique de Toulouse, UniversitØ Paul Sabatier, Toulouse Cedex
Venue:
IFSA'03 Proceedings of the 10th international fuzzy systems association World Congress conference on Fuzzy sets and systems
Year:
2003

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Abstract

This paper presents an alternative to precise analytical modelling, by means of imprecise interpolative models. The model specification is based on gradual rules that express constraints that govern the interpolation mechanism. The modelling strategy is applied to the classification of time series. In this context, it is shown that good recognition performance can be obtained with models that are highly imprecise.