This chapter emphasizes on the role played by rough set theory (RST) within the broad field
of Machine Learning (ML). As a sound data analysis and knowledge discovery paradigm …

ABSTRACT The Rough Set (RS) theory can be considered as a tool to reduce the input
dimensionality and to deal with vagueness and uncertainty in datasets. Over the years, there …

The scale of the radius for constructing neighborhood relation has a great effect on the
results of neighborhood rough sets and corresponding measures. A very small radius …

The package RoughSets, written mainly in the R language, provides implementations of
methods from the rough set theory (RST) and fuzzy rough set theory (FRST) for data …

The aim of this paper is to give rudiments of rough set theory and present some recent
research directions proposed by the author. Rough set theory is a new mathematical …

Rough set data analysis (RSDA) was introduced to Computer Science in the early 1980s by
Z. Pawlak [Paw82] and has since come into focus as an alternative to the more widely used …

Worldwide, there has been a rapid growth in interest in rough set theory and its applications
in recent years. Evidence of this can be found in the increasing number of high-quality …

We consider a rough-set-inspired framework for deriving feature subset ensembles from
data. Each of feature subsets yields a single classifier, basically by generating its …

Big data, artificial intelligence and the Internet of things (IoT) are still very popular areas in
current research and industrial applications. Processing massive amounts of data generated …

Along the years, rough set theory has earned a well-deserved reputation as a sound
methodology for dealing with imperfect knowledge in a simple though mathematically sound …