Double representation of information and hybrid combination for identification systems
Alain Nifle, Roger Reynaud · 2000
In an increasing number of classification systems, a-priori information and observations are both present as probabilistic and possibilistic continuous distributions for representing information in the most accurate and reliable way. We propose a method where information is simultaneously modelled in terms of probability and possibility and is combined in a hybrid manner, without changing it either in an entirely probabilistic form or in an entirely possibilistic form. It thus defines a compatibility mass function. To extend the mechanism of validation windowing found in tracking algorithms, a possibilistic distribution is associated with each continuous probabilistic distribution, and this contributes to building a mass function related to the validation/rejection of the observation. Thanks to these hybrid combination mechanisms, we build a classifier respecting some constraints in the framework of evidence theory. In the paper, we describe the classifier and discuss its properties.