This issue is dedicated to Professor Lotfi A. Zadeh, the founder of fuzzy set theory, on the occasion of celebrating 50 years of his first publication on fuzzy sets in 1965 [1]. The idea of fuzzy sets has influenced a very large number of researchers all over the world. Since 1965 many thousands of research papers on fuzzy set theory and its applications have been published as well as many thou...

Recently, the rough set and fuzzy set theory have generated a great deal of interest among more and more researchers. Granular computing (GrC) is an emerging computing paradigm of information processing and an approach for knowledge representation and data mining. The purpose of granular computing is to seek for an approximation scheme which can effectively solve a complex problem at a certain ...

Pawlak’s Rough set theory was originally proposed as a general mathematical tool for dealing with uncertainty in modeling imperfect knowledge. The purpose of this paper is to introduce the concept of multifuzzy rough sets by combining the multi-fuzzy set and rough set models. Some operations such as Complement, Union, Intersection etc. are defined for multi-fuzzy rough sets and De Morgan’s laws...

Just like rough set theory, fuzzy set theory addresses the topic of dealing with imperfect knowledge. Recent investigations have shown how both theories can be combined into a more flexible, more expressive framework for modelling and processing incomplete information in information systems. At the same time, intuitionistic fuzzy sets have been proposed as an attractive extension of fuzzy sets,...

Decision table reduction in KDD refers to the problem of selecting those input feature values that are most predictive of a given outcome by reducing a decision table like database from both vertical and horizontal directions. Fuzzy rough sets has been proven to be a useful tool of attribute reduction (i.e. reduce decision table from vertical direction). However, relatively less researches on d...