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The hybridization of rough sets and fuzzy sets has focused on creating an end product that extends both contributing computing paradigms in a conservative way. As a result, the hybrid theory inherits their respective strengths, but also exhibits some weaknesses. In particular , although they allow for gradual membership, fuzzy rough sets are still abrupt in(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)
—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)
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)
In this paper, we propose a nearest neighbour algorithm that uses the lower and upper approximations from fuzzy rough set theory in order to classify test objects, or predict their decision value. It is shown experimentally that our method outperforms other nearest neighbour approaches (classical, fuzzy and fuzzy-rough ones) and that it is competitive with(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)
Most existing recommender systems employ collaborative filtering (CF) techniques in making projections about which items an e-service user is likely to be interested in, i.e. they identify correlations between users and recommend items which similar users have liked in the past. Traditional CF techniques, however, have difficulties when confronted with(More)