Fuzzy object-induced network three-way concept lattice and its attribute reduction

IF 3.2 3区计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

International Journal of Approximate Reasoning Pub Date : 2024-07-15 DOI:10.1016/j.ijar.2024.109251

Miao Liu , Ping Zhu

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引用次数: 0

Abstract

Concept cognition and knowledge discovery under network data combine formal concept analysis with complex network analysis. However, in real life, network data is uncertain due to some limitations. Fuzzy sets are a powerful tool to deal with uncertainty and imprecision. Therefore, this paper focuses on concept-cognitive learning in fuzzy network formal contexts. Fuzzy object-induced network three-way concept (network OEF-concept) lattices and their properties are mainly investigated. In addition, three fuzzy network weaken-concepts are proposed. As the real data is too large, attribute reduction can simplify concept-cognitive learning by removing redundant attributes. Thus, the paper proposes attribute reduction methods that can keep the concept lattice structure isomorphic and the set of extents of granular concepts unchanged. Finally, an example is given to show the attribute reduction process of a fuzzy network three-way concept lattice. Attribute reduction experiments are conducted on nine datasets, and the results prove the feasibility of attribute reduction.

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模糊客体诱导网络三向概念网格及其属性还原

网络数据下的概念认知和知识发现结合了正式的概念分析和复杂的网络分析。然而，在现实生活中，网络数据由于某些局限性而具有不确定性。模糊集是处理不确定性和不精确性的有力工具。因此，本文重点研究模糊网络形式语境下的概念认知学习。主要研究了模糊对象诱导网络三向概念（网络 OEF-概念）网格及其特性。此外，还提出了三种模糊网络弱化概念。由于真实数据过于庞大，属性缩减可以通过去除冗余属性来简化概念认知学习。因此，本文提出了可以保持概念网格结构同构和颗粒概念外延集不变的属性缩减方法。最后，举例说明了模糊网络三向概念网格的属性缩减过程。在九个数据集上进行了属性缩减实验，结果证明了属性缩减的可行性。

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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.