Distinguishing vagueness from ambiguity in rough set approximations

Salvatore Greco, Benedetto Matarazzo, Roman Slowinski

Research output: Contribution to journalArticlepeer-review

90 Downloads (Pure)

Abstract

In this paper we present a new approach to rough set approximations that permits to distinguish between two kinds of \imperfect" knowledge in a joint framework: on one hand, vagueness, due to imprecise knowledge and uncertainty typical of fuzzy sets, and on the other hand, ambiguity, due to granularity of knowledge originating from the coarseness typical of rough sets. The basic idea of our approach is that each concept is represented by an orthopair, that is, a pair of disjoint sets in the universe of knowledge. The first set in the pair contains all the objects that are considered as surely belonging to the concept, while the second set contains all the objects that surely do not belong to the concept. In this context, following some previous research conducted by us on the algebra of rough sets, we propose to define as rough approximation of the orthopair representing the considered concept another orthopair composed of lower approximations of the two sets in the first orthopair. We shall apply this idea to the classical rough set approach based on indiscernibility, as well as to the dominance-based rough set approach. We discuss also a variable precision rough approximation, and a fuzzy rough approximation of the orthopairs. Some didactic examples illustrate the proposed methodology.
Original languageEnglish
Pages (from-to)89-125
Number of pages36
JournalInternational Journal of Uncertainty, Fuzziness and Knowledge Based Systems
Volume26
Issue numberSupplement 2
DOIs
Publication statusPublished - 31 Dec 2018

Keywords

  • Imperfect knowledge
  • Vagueness
  • Ambiguity
  • Rough sets
  • Dominance-based Rough Set Approach
  • Pawlak operator
  • Fuzzy rough approximations
  • Variable precision rough approximations

Fingerprint

Dive into the research topics of 'Distinguishing vagueness from ambiguity in rough set approximations'. Together they form a unique fingerprint.

Cite this