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Among the various extensions to the common [0, 1]-valued truth degrees of " traditional " fuzzy set theory, closed intervals of [0, 1] stand out as a particularly appealing and promising choice for representing imperfect information, nicely accommodating and combining the facets of vagueness and uncertainty without paying too much in terms of computational(More)
—Intuitionistic fuzzy sets form an extension of fuzzy sets: while fuzzy sets give a degree to which an element belongs to a set, intuitionistic fuzzy sets give both a membership degree and a nonmembership degree. The only constraint on those two degrees is that their sum must be smaller than or equal to 1. In fuzzy set theory, an important class of(More)
With the demand for knowledge-handling systems capable of dealing with and distinguishing between various facets of imprecision ever increasing, a clear and formal characterization of the mathematical models implementing such services is quintessen-tial. In this paper, this task is undertaken simultaneously for the definition of implication within two(More)
—Traditional rough set theory uses equivalence relations to compute lower and upper approximations of sets. The corresponding equivalence classes either coincide or are disjoint. This behaviour is lost when moving on to a fuzzy T-equivalence relation. However, none of the existing studies on fuzzy rough set theory tries to exploit the fact that an element(More)
Trust and distrust are two increasingly important metrics in social networks, reflecting users' attitudes and relationships towards each other. In this paper , we study the indirect derivation of these metrics' values for users that do not know each other, but are connected through the network. In particular, we study bilattice-based aggregation approaches(More)
Trust networks among users of a recommender system (RS) prove beneficial to the quality and amount of the recommendations. Since trust is often a gradual phenomenon , fuzzy relations are the pre-eminent tools for modeling such networks. However, as current trust-enhanced RSs do not work with the notion of distrust, they cannot differentiate unknown users(More)
Traditionally, membership to the fuzzy-rough lower, resp. upper approximation is determined by looking only at the worst, resp. best performing object. Consequently, when applied to data analysis problems, these approximations are sensitive to noisy and/or outlying samples. In this paper, we advocate a mitigated approach, in which membership to the lower(More)