Granular rough sets and granular shadowed sets: Three-way approximations in Pawlak approximation spaces

IF 3.2 3区 计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE International Journal of Approximate Reasoning Pub Date : 2022-03-01 DOI:10.1016/j.ijar.2021.11.012
Yiyu Yao , Jilin Yang
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引用次数: 10

Abstract

A Pawlak approximation space is a pair of a ground set/space and a quotient set/space of the ground set induced by an equivalence relation on the ground set. The quotient space is a simple granulation of the ground space such that an equivalence class is a granule of objects in the ground space and, at the same time, a single granular object in the quotient space. The new two-space view leads to more insights into and a deeper understanding of rough set theory. In this paper, we revisit results from rough sets from the two-space perspective and introduce the notions of granular rough sets and probabilistic granular rough sets in the quotient space, as three-way approximations of sets in the ground space. We propose a concept of granular shadowed sets in the quotient space, as three-way approximations of fuzzy sets in the ground space. We formulate a cost-sensitive method to construct a granular shadowed set from a fuzzy set. We show that, when the costs satisfy some conditions, the three granular approximations become the same for the special case where a fuzzy set is in fact a set.

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颗粒粗糙集和颗粒阴影集:Pawlak近似空间中的三向逼近
Pawlak近似空间是由基集上的等价关系导出的基集/空间和基集的商集/空间的一对。商空间是地空间的简单粒化,使得等价类是地空间中物体的粒化,同时是商空间中的单个粒化物体。新的两个空间的观点导致更多的见解和更深入的理解粗糙集理论。本文从二空间的角度重新审视粗糙集的结果,并引入商空间中的颗粒粗糙集和概率颗粒粗糙集的概念,作为地面空间中集合的三向逼近。我们提出了商空间中的颗粒阴影集的概念,作为地面空间中模糊集的三向逼近。我们提出了一种代价敏感的方法来从模糊集构造颗粒阴影集。我们证明,当成本满足某些条件时,对于模糊集实际上是一个集的特殊情况,三个颗粒近似是相同的。
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来源期刊
International Journal of Approximate Reasoning
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.
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