In this paper, we investigate the extraction of fuzzy rules from data tables based on possibility theory. A possibilistic decision language is used to represent the extracted fuzzy rules. The algorithm for rule extraction is presented and the complexity analysis is carried out. Since the results of the rule induction process strongly depend on the representation language, we also discuss some a...

When there is uncertainty about the values of a variable, it is possible to have different pieces of information about it. This can be due to the fact that they represent the points of view of different agents and these agents have followed different criteria when codify the pieces of information by means of numbers. The aim of this paper is to provide tools for the comparison of these represen...

When merging belief functions, Dempster rule of combination is justified only when sources can be considered as independent. When dependencies are ill-known, it is usual to ask the merging operation to satisfy the property of idempotence, as this property ensures a cautious behaviour in the face of dependent sources. There are different strategies to find such rules for belief functions. One st...

We overview the existing conditional independence concepts from the viewpoint of their relation to possibility theory. We will show that a suitable notion — from this perspective — seems to be strong independence in the wider framework of credal sets.

Possibility is considered as a promising mathematical tool to represent that particular case of incomplete information given by a measurement result. A measurement result can be affected by systematic and random effects. The combination of random contributions in the possibility theory returns only approximated results, and it is important to identify an optimal combination method to obtain acc...

Predictive accuracy is the sum of two kinds of uncertainty–natural variability and modeling uncertainty. This paper addresses the quantification of predictive accuracy of complex simulation models from two perspectives. First, it recognizes that there is a difference between variability and modeling uncertainty; the former can not be reduced with more test information, while the latter can. We ...