This paper introduces a neural network architecture based on rough sets and rough membership functions. The neurons of such networks instantiate approximate reasoning in assessing ...
James F. Peters, Andrzej Skowron, Liting Han, Shee...
Abstract. We provide the unified methodology for searching for approximate decision reducts based on rough membership distributions. Presented study generalizes well known relation...
Interval computations estimate the uncertainty of the result of data processing in situations in which we only know the upper bounds ∆ on the measurement errors. In interval comp...
In this paper we present a multi-scale method based on the hybrid notion of rough fuzzy sets, coming from the combination of two models of uncertainty like vagueness by handling r...
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 t...