scispace - formally typeset
Open AccessJournal ArticleDOI

Relational interpretations of neighborhood operators and rough set approximation operators

Yiyu Yao
- 01 Nov 1998 - 
- Vol. 111, Iss: 1, pp 239-259
Reads0
Chats0
TLDR
This paper presents a framework for the formulation, interpretation, and comparison of neighborhood systems and rough set approximations using the more familiar notion of binary relations, and introduces a special class of neighborhood system, called 1-neighborhood systems.
About
This article is published in Information Sciences.The article was published on 1998-11-01 and is currently open access. It has received 967 citations till now. The article focuses on the topics: Operator theory & Rough set.

read more

Citations
More filters
Journal ArticleDOI

An application of soft sets in a decision making problem

TL;DR: In this article, the theory of soft sets was applied to solve a decision-making problem using rough mathematics, and the results showed that soft sets can be used to solve decision making problems.
Journal ArticleDOI

Neighborhood rough set based heterogeneous feature subset selection

TL;DR: A neighborhood rough set model is introduced to deal with the problem of heterogeneous feature subset selection and Experimental results show that the neighborhood model based method is more flexible to deals with heterogeneous data.
Journal ArticleDOI

Reduction and axiomization of covering generalized rough sets

TL;DR: It has been proved that the reduct of a covering is the minimal covering that generates theSame covering lower approximation or the same covering upper approximation, so this concept is also a technique to get rid of redundancy in data mining.
Journal ArticleDOI

Topological approaches to covering rough sets

TL;DR: This paper explores the topological properties of covering-based rough sets, studies the interdependency between the lower and the upper approximation operations, and establishes the conditions under which two coverings generate the same lower approximation operation and the same upper approximation operation.
Journal ArticleDOI

Generalized fuzzy rough sets

TL;DR: This paper presents a general framework for the study of fuzzy rough sets in which both constructive and axiomatic approaches are used and the connections between fuzzy relations and fuzzy rough approximation operators are examined.
References
More filters
Book

Universal Algebra

Book

Modal Logic: An Introduction

TL;DR: This chapter discusses standard models for modal logics, classical systems of modal logic, and Determination and decidability for classical systems.
Book

Intelligent Decision Support: Handbook of Applications and Advances of the Rough Sets Theory

TL;DR: The use of 'Rough Sets' Methods to draw Premonitory Factors for Earthquakes by emphasising Gas Geochemistry: The Case of a Low Seismic Activity Context in Belgium J.T. Polkowski is used.
Book

Fuzzy Logic for the Management of Uncertainty

TL;DR: Partial table of contents:Issues in the MANAGEMENT of UNCERTAINty A Survey of Uncertain and Approximate Inference.
Journal ArticleDOI

Two views of the theory of rough sets in finite universes

TL;DR: This paper presents and compares two views of the theory of rough sets: the operator-oriented and set-oriented views, which interprets rough set theory as an extension of set theory with two additional unary operators.
Frequently Asked Questions (7)
Q1. What have the authors contributed in "Relational interpretations of neighborhood operators and rough set approximation operators" ?

This paper presents a framework for the formulation, interpretation, and comparison of neighborhood systems and rough set approximations using the more familiar notion of binary relations. Properties of neighborhood and approximation operators are studied, and their connections are examined. 

The theory of rough sets is motivated by practical needs in classification, concept formation, and data analysis with insufficient and incomplete information [12–15]. 

The authors define operations on binary relations through set-theoretic operations:∼R = {(x, y) | not xRy},R ∩ Q = {(x, y) | xRy and xQy},R ∪ Q = {(x, y) | xRy or xQy}. 

A set of necessary and sufficient condition for (P) is:(P1) n is inverse serial,(P2) for all x, y ∈ U, either n(x) = n(y) or n(x) ∩ n(y) = ∅.Condition (P1) is equivalent to saying that ⋃x∈U n(x) = U . 

For this generalization, only the neighborhood of x is used to decide the memberships of x in the lower and upper approximation of a subset X. 

According to property (U2) and equations (15) and (16), upper approximation operators can be interpreted using neighborhood operators:aprRp(X) = Rs(X), aprRs(X) = Rp(X), aprRp∧s(X) = Rp∧s(X), aprRp∨s(X) = Rp∨s(X). 

For a relation R and its inverse R−1, the application of operators ∼, ∩, and ∪ produces 16 different relations, such as ∼R, ∼R−1, and R ∪ ∼R−1.