Knowledge reduction of dynamic covering decision information systems with varying attribute values

TL;DR

This paper introduces incremental algorithms for knowledge reduction in dynamic covering decision systems with changing attribute values, improving efficiency in computing set approximations.

Contribution

It presents novel incremental methods for computing characteristic matrices and set approximations in dynamic covering information systems.

Findings

Incremental algorithms are effective for dynamic systems.

Experimental results confirm efficiency improvements.

Knowledge reduction is successfully performed using the proposed methods.

Abstract

Knowledge reduction of dynamic covering information systems involves with the time in practical situations. In this paper, we provide incremental approaches to computing the type-1 and type-2 characteristic matrices of dynamic coverings because of varying attribute values. Then we present incremental algorithms of constructing the second and sixth approximations of sets by using characteristic matrices. We employ experimental results to illustrate that the incremental approaches are effective to calculate approximations of sets in dynamic covering information systems. Finally, we perform knowledge reduction of dynamic covering information systems with the incremental approaches.