Related families-based attribute reduction of dynamic covering information systems with variations of object sets

TL;DR

This paper presents methods for attribute reduction in dynamic covering decision systems using related families, effectively handling object set variations through addition and deletion, with demonstrated experimental success.

Contribution

It introduces novel related family-based mechanisms for attribute reduction in dynamic covering decision systems with object set changes.

Findings

Methods are effective for attribute reduction with object additions.

Methods are effective for attribute reduction with object deletions.

Experimental results confirm the approach's efficiency.

Abstract

In practice, there are many dynamic covering decision information systems, and knowledge reduction of dynamic covering decision information systems is a significant challenge of covering-based rough sets. In this paper, we first study mechanisms of constructing attribute reducts for consistent covering decision information systems when adding objects using related families. We also employ examples to illustrate how to construct attribute reducts of consistent covering decision information systems when adding objects. Then we investigate mechanisms of constructing attribute reducts for consistent covering decision information systems when deleting objects using related families. We also employ examples to illustrate how to construct attribute reducts of consistent covering decision information systems when deleting objects. Finally, the experimental results illustrates that the related family-based methods are effective to perform attribute reduction of dynamic covering decision information systems when object sets are varying with time.