An improved approach to attribute reduction with covering rough sets

TL;DR

This paper introduces a simplified discernibility matrix for covering rough sets, significantly reducing computational complexity while maintaining equivalence to previous methods, thereby enhancing attribute reduction efficiency in data analysis.

Contribution

It presents an improved, simpler discernibility matrix for covering rough sets, reducing computational complexity in attribute reduction processes.

Findings

The new discernibility matrix is equivalent to the previous one.

Computational complexity of attribute reduction is greatly reduced.

The approach enhances efficiency in data preprocessing for pattern recognition.

Abstract

Attribute reduction is viewed as an important preprocessing step for pattern recognition and data mining. Most of researches are focused on attribute reduction by using rough sets. Recently, Tsang et al. discussed attribute reduction with covering rough sets in the paper [E. C.C. Tsang, D. Chen, Daniel S. Yeung, Approximations and reducts with covering generalized rough sets, Computers and Mathematics with Applications 56 (2008) 279-289], where an approach based on discernibility matrix was presented to compute all attribute reducts. In this paper, we provide an improved approach by constructing simpler discernibility matrix with covering rough sets, and then proceed to improve some characterizations of attribute reduction provided by Tsang et al. It is proved that the improved discernible matrix is equivalent to the old one, but the computational complexity of discernible matrix is greatly reduced.