Semantics-preserving dimensionality reduction refers to the problem of selecting those input
features that are most predictive of a given outcome; a problem encountered in many areas …

The rough and fuzzy set approaches presented here open up many new frontiers for
continued research and development Computational Intelligence and Feature Selection …

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 …

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Rough set theory can be applied to rule induction. There are two different types of
classification rules, positive and boundary rules, leading to different decisions and …

The two-volume set of proceedings of IJCRS 2017, the 2017 International Joint Conference
on Rough Sets, contains the results of the meeting of the International Rough Set Society …

Attribute reduction plays an important role in the areas of rough sets and granular
computing. Many kinds of attribute reducts have been defined in previous studies. However …

We use information entropy measure to extend the rough set based notion of a reduct. We
introduce the Approximate Entropy Reduction Principle (AERP). It states that any …

A relative reduct can be considered as a minimum set of attributes that preserves a certain
classification property. This paper investigates three different classification properties, and …

In the research spectrum of rough set, the task of attribute reduction is obtaining a minimal
attribute subset that preserves certain properties of the original data. Cost-sensitive attribute …