A rapid growth of interest in rough set theory [297] and its applications can be lately seen in
the number of international workshops, conferences and seminars that are either directly …

The study introduces and discusses a principle of justifiable granularity, which supports a
coherent way of designing information granules in presence of experimental evidence …

The rule extraction capability of neural networks is an issue of interest to many researchers.
Even though neural networks offer high accuracy in classification and prediction, there are …

We are concerned with formal models of reasoning under uncertainty. Many approaches to
this problem are known in the literature: Dempster-Shafer theory, bayesian-based …

Rough set theory models similarities and differences of objects based on the notions of
indiscernibility and discernibility. With respect to any subset of attributes, one can define two …

An importance of the idea of granularity of knowledge for approximate reasoning has been
stressed in Pawlak (1997) and Zadeh (1966, 1997). We address here the problem of …

Subsethood measures, also known as set-inclusion measures, inclusion degrees, rough
inclusions, and rough-inclusion functions, are generalizations of the set-inclusion relation for …

Approximations in rough sets theory are important operators to discover interesting patterns
and dependencies in data mining. Both certain and uncertain rules are unraveled from …

Rough Mereology is a paradigm allowing for a synthesis of main ideas of two potent
paradigms for reasoning under uncertainty: Fuzzy Set Theory and Rough Set Theory …

Set-valued data are a common type of data for characterizing uncertain and missing
information. Traditional dominance-based rough sets can not efficiently deal with large-scale …