Three-way concept lattice from adjunctive positive and negative concepts

IF 3.2 3区 计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Binghan Long , Tingquan Deng , Yiyu Yao , Weihua Xu
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引用次数: 0

Abstract

Three-way concept lattices (TCLs) have been widely explored due to their clear hierarchical structures, concise visual description and good interpretability. In contrast to classic formal contexts, lattice-valued fuzzy contexts exhibit great capability in describing and representing concepts with uncertainty. Different from conventional approaches to research of TCLs, this paper focuses on investigating the algebraic structure and properties of three-way concept lattice (TCL) stemmed from the positive concept lattice and negative concept lattice in a lattice-valued formal context. Several associated concept lattices such as the Cartesian product of positive concept lattice and negative lattice (i.e., pos-neg lattice), lattices induced from the partition of the pos-neg lattice, and their relationship are explored. Specifically, the isomorphism, embedding and order-preserving mappings between them are built. The quotient set of pos-neg lattice when being defined a specific equivalence relation on it is a complete lattice and each equivalence class is a lower semi-lattice. It is further declared that the structure of TCL is intrinsically and determined wholly by the pos-neg lattice. A practical application of the built theory of TCL is provided to sort alternatives in multi-criteria decision making.

来自正反概念的三向概念网格
三向概念网格(TCL)因其清晰的层次结构、简洁的视觉描述和良好的可解释性而被广泛探讨。与经典的形式语境相比,格值模糊语境在描述和表示具有不确定性的概念方面表现出了强大的能力。与传统的 TCL 研究方法不同,本文重点研究了在格值形式语境中由正概念格和负概念格衍生出的三向概念格(TCL)的代数结构和性质。本文探讨了正概念网格和负概念网格的笛卡尔乘积(即正负网格)、正负网格分割诱导出的网格等几种相关概念网格及其关系。具体来说,建立了它们之间的同构、嵌入和保序映射。当在正负网格上定义特定的等价关系时,它的商集是一个完全网格,每个等价类都是一个下半网格。这进一步说明,TCL 的结构本质上完全由正负格决定。建立的 TCL 理论在实际应用中可用于多标准决策中的备选方案排序。
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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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