Attribute reduction with pessimistic multigranulation rough sets in relation systems

IF 3 3区计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

International Journal of Approximate Reasoning Pub Date : 2025-06-13 DOI:10.1016/j.ijar.2025.109515

Yehai Xie

引用次数: 0

Abstract

Pessimistic multigranulation rough sets (PMGRSs) are an important extension of rough sets and attribute reduction is a significant application of rough set theory. In this paper, we study attribute reduction using PMGRSs in relation systems. Recognizing that the assumptions of reflexivity-symmetry and equivalence of relations are obstacles for application, we redefine the concepts of pessimistic reduction, pessimistic lower approximate distribution (PLAD) reduction, and pessimistic upper approximate distribution (PUAD) reduction based on relations without any restrictions. Furthermore, we design reduction algorithms based on discernibility matrices to identify all pessimistic reducts, PLAD-reducts, and PUAD-reducts. Finally, we conducted comparative experiments on 18 public datasets and the experimental results confirmed the effectiveness of the proposed algorithms.

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关系系统中悲观多粒粗糙集的属性约简

悲观多粒粗糙集是粗糙集的重要扩展，属性约简是粗糙集理论的重要应用。本文研究了关系系统中pmgrs的属性约简方法。认识到关系的自反对称性和等价性假设是应用的障碍，我们重新定义了基于关系的悲观约简、悲观下近似分布（PLAD）约简和悲观上近似分布（PUAD）约简的概念。此外，我们设计了基于可分辨矩阵的约简算法，以识别所有悲观约简、plad约简和puad约简。最后，我们在18个公共数据集上进行了对比实验，实验结果证实了所提出算法的有效性。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

International Journal of Approximate Reasoning 工程技术-计算机：人工智能

CiteScore

6.90

自引率

12.80%

发文量

170

审稿时长

67 days

期刊介绍： The International Journal of Approximate Reasoning is intended to serve as a forum for the treatment of imprecision and uncertainty in Artificial and Computational Intelligence, covering both the foundations of uncertainty theories, and the design of intelligent systems for scientific and engineering applications. It publishes high-quality research papers describing theoretical developments or innovative applications, as well as review articles on topics of general interest. Relevant topics include, but are not limited to, probabilistic reasoning and Bayesian networks, imprecise probabilities, random sets, belief functions (Dempster-Shafer theory), possibility theory, fuzzy sets, rough sets, decision theory, non-additive measures and integrals, qualitative reasoning about uncertainty, comparative probability orderings, game-theoretic probability, default reasoning, nonstandard logics, argumentation systems, inconsistency tolerant reasoning, elicitation techniques, philosophical foundations and psychological models of uncertain reasoning. Domains of application for uncertain reasoning systems include risk analysis and assessment, information retrieval and database design, information fusion, machine learning, data and web mining, computer vision, image and signal processing, intelligent data analysis, statistics, multi-agent systems, etc.