Attribute reduction algorithm of rough sets based on spatial optimization

TL;DR

This paper proposes a novel rough set attribute reduction algorithm that incorporates spatial similarity to produce more concise and general rules, outperforming traditional methods on various datasets.

Contribution

It introduces a spatial optimization-based approach to attribute reduction in rough sets, enhancing rule conciseness and generality compared to existing algorithms.

Findings

Significant improvement in rule conciseness and generality.

Higher spatial similarity between reduced and decision attributes.

Effective on multiple datasets compared to traditional algorithms.

Abstract

Rough set is one of the important methods for rule acquisition and attribute reduction. The current goal of rough set attribute reduction focuses more on minimizing the number of reduced attributes, but ignores the spatial similarity between reduced and decision attributes, which may lead to problems such as increased number of rules and limited generality. In this paper, a rough set attribute reduction algorithm based on spatial optimization is proposed. By introducing the concept of spatial similarity, to find the reduction with the highest spatial similarity, so that the spatial similarity between reduction and decision attributes is higher, and more concise and widespread rules are obtained. In addition, a comparative experiment with the traditional rough set attribute reduction algorithms is designed to prove the effectiveness of the rough set attribute reduction algorithm based on spatial optimization, which has made significant improvements on many datasets.