Attribute Reduction in Multi-source Decision Systems

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. Code Availability: https://github.com/nkjmnkjm/SRS.

Uncertainty Measure for Multisource Intuitionistic Fuzzy Information System

“…Literature [31] proposed a fuzzy multigranulation decision-theoretic rough set model in multisource fuzzy information systems. Literature [32] investigated the attribute reduction in multisource decision systems. Literature [33] combined rough set model and multisource decision systems and established a decision-theoretic rough set model of multisource decision systems.…”

Lower Approximation Reduction Based on Discernibility Information Tree in Inconsistent Ordered Decision Information Systems

“…Attribute reduction [18][19][20][21][22] is one of the core contents of knowledge discovery. It describes how to delete unnecessary knowledge in an information system, to reduce the quantity of information to be processed in data mining and improve the efficiency of data mining.…”