Ranking functions are qualitative degrees of uncertainty ascribed to events charged by uncertainty and taking as their values non-negative integers in the sense of ordinal numbers. Introduced are ranking functions induced by real-valued possibilistic measures and it is shown that different possibilistic measures with identical ranking functions yield the same results when applied in decision pr...

In standard possibilistic logic, prioritized in­ formation are encoded by means of weighted knowledge bases. This paper proposes an exten­ sion of possibilistic logic to deal with partially ordered information which can be viewed as a family of possibilistic knowledge bases. We show that all basic notions of possibilis­ tic logic have natural counterparts when dealing with partially ordered inf...

In this paper, application of possibilistic clustering techniques to identification of local linear models will be discussed. In particular, a generalisation of some possibilistic algorithms in the bibliography is obtained. With the presented procedures, a trade-off between an “expected shape” of the membership functions and model fit can be stated. Possibilistic clustering may allow for better...

Uncertainty reasoning and inconsistency handling are two important problems that often occur in the applications of the Semantic Web. Possibilistic description logics provide a flexible framework for representing and reasoning with ontologies where uncertain and/or inconsistent information exists. Based on our previous work, we develop a possibilistic description logic reasoner. Our demo will i...

The intended purpose of this research 1 is i) to show the specificity of possibilistic logic with respect to other logics with weighted statements, in particular its ability to deal both with uncertainty and vagueness, ii) to develop proof methods for possibilistic logic, and iii) to relate its ability to cope with partial inconsistency to belief revision and non-monotonic reasoning issues. See...

In this paper, an analysis of the convergence performance is conducted for a class of possibilistic clustering algorithms utilizing the Zangwill convergence theorem. It is shown that under certain conditions the iterative sequence generated by a possibilistic clustering algorithm converges, at least along a subsequence, to either a local minimizer or a saddle point of the objective function of ...

Possibilistic networks offer a qualitative approach for modeling epistemic uncertainty. Their practical implementation requires the specification of conditional possibility tables, as in the case of Bayesian networks for probabilities. This paper presents the possibilistic counterparts of the noisy probabilistic connectives (and, or, max, min, . . . ). Their interest is illustrated on an exampl...

This paper continues the authors’ research in stability analysis in possibilistic programming in that it extends the results in [7] to possibilistic linear programs with multiple objective functions. Namely, we show that multiobjective possibilistic linear programs with continuous fuzzy number coefficients are well-posed, i.e. small changes in the membership function of the coefficients may cau...

It is found that sub-pixel classifiers for classification of multi-spectral remote sensing data yield a higher accuracy. With this objective, a study has been carried out, where fuzzy set theory based sub-pixel classifiers have been compared with statistical based sub-pixel classifier for classification of multi-spectral remote sensing data.Although, a number of Fuzzy set theory based classifie...