The problem considered in this paper is the evaluation of perception as a means of
optimizing various tasks. The solution to this problem hearkens back to early research on …

We discuss approximation spaces in the granular computing framework. Such
approximation spaces generalise the approaches to concept approximation existing in …

Granulation of a universe involves grouping of similar elements into granules. With
granulated views, we deal with approximations of concepts, represented by subsets of the …

The results reported in this paper create a step toward the rough set-based foundations of
data mining and machine learning. The approach is based on calculi of approximation …

This chapter briefly introduces the highlights of the rough set approach to knowledge
discovery by means of various forms of information granulation. This approach has its roots …

Granular Computing as a paradigm in Approximate Reasoning is concerned with
granulation of available knowledge into granules that consists of entities similar in …

We discuss how approximation spaces considered in the context of rough sets and
information granule theory have evolved over the last 20 years from simple approximation …

The aim of the paper is to present basic notions related to granular computing, namely the
information granule syntax and semantics as well as the inclusion and closeness (similarity) …

The paradigm of granular rough computing has risen quite recently—was initiated by
Professor Lotfi Zadeh in 1979. This paradigm is strictly connected with the Rough Set …

We discuss some generalizations of the approximation space definition introduced in 1994
[24, 25]. These generalizations are motivated by real-life applications. Rough set based …