{"title":"结合关系系统和邻域系统方法的粗糙集模型的形式化研究","authors":"Md. Aquil Khan, Ranjan","doi":"10.1016/j.ijar.2025.109492","DOIUrl":null,"url":null,"abstract":"<div><div>This article introduces a rough set model that integrates two approximation operators - one induced by a relation and the other by a neighbourhood system - within a unified framework. The proposed model formalizes concept approximation in heterogeneous settings where multiple granulation mechanisms contribute to information processing. The framework effectively combines the strengths of relation-based and neighbourhood-based rough set models by employing conjunctive and disjunctive fusion rules. To provide a rigorous foundation, we develop a logical study of the resulting approximation operators using the modal language with two unary modal operators. We introduce semantics that fuses Kripke and neighbourhood interpretations of modal operators, establish sound and complete deductive systems, and investigate definability properties. This study contributes to both rough set theory and modal logic by offering a formal perspective on the fusion of approximation operators.</div></div>","PeriodicalId":13842,"journal":{"name":"International Journal of Approximate Reasoning","volume":"186 ","pages":"Article 109492"},"PeriodicalIF":3.0000,"publicationDate":"2025-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A formal study of a rough set model integrating relational and neighbourhood system approaches\",\"authors\":\"Md. Aquil Khan, Ranjan\",\"doi\":\"10.1016/j.ijar.2025.109492\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>This article introduces a rough set model that integrates two approximation operators - one induced by a relation and the other by a neighbourhood system - within a unified framework. The proposed model formalizes concept approximation in heterogeneous settings where multiple granulation mechanisms contribute to information processing. The framework effectively combines the strengths of relation-based and neighbourhood-based rough set models by employing conjunctive and disjunctive fusion rules. To provide a rigorous foundation, we develop a logical study of the resulting approximation operators using the modal language with two unary modal operators. We introduce semantics that fuses Kripke and neighbourhood interpretations of modal operators, establish sound and complete deductive systems, and investigate definability properties. This study contributes to both rough set theory and modal logic by offering a formal perspective on the fusion of approximation operators.</div></div>\",\"PeriodicalId\":13842,\"journal\":{\"name\":\"International Journal of Approximate Reasoning\",\"volume\":\"186 \",\"pages\":\"Article 109492\"},\"PeriodicalIF\":3.0000,\"publicationDate\":\"2025-06-11\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"International Journal of Approximate Reasoning\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0888613X25001331\",\"RegionNum\":3,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Approximate Reasoning","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0888613X25001331","RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
A formal study of a rough set model integrating relational and neighbourhood system approaches
This article introduces a rough set model that integrates two approximation operators - one induced by a relation and the other by a neighbourhood system - within a unified framework. The proposed model formalizes concept approximation in heterogeneous settings where multiple granulation mechanisms contribute to information processing. The framework effectively combines the strengths of relation-based and neighbourhood-based rough set models by employing conjunctive and disjunctive fusion rules. To provide a rigorous foundation, we develop a logical study of the resulting approximation operators using the modal language with two unary modal operators. We introduce semantics that fuses Kripke and neighbourhood interpretations of modal operators, establish sound and complete deductive systems, and investigate definability properties. This study contributes to both rough set theory and modal logic by offering a formal perspective on the fusion of approximation operators.
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.