Article
Computer Science, Artificial Intelligence
Muhammad Akram, Hafiza Saba Nawaz, Muhammet Deveci
Summary: This paper explores the application of Pythagorean fuzzy set theory in formal concept analysis, defining Pythagorean fuzzy formal concepts and Pythagorean fuzzy concept lattices. It also discusses knowledge reduction, information granulation, and attribute reduction in Pythagorean fuzzy formal contexts.
EXPERT SYSTEMS WITH APPLICATIONS
(2023)
Article
Multidisciplinary Sciences
Ning Lan, Shuqun Yang, Ling Yin, Yongbin Gao
Summary: The application of knowledge graphs is limited in some domains due to the high reliability of knowledge required. Traditional knowledge engineering has high correctness but low efficiency, which calls for an organic connection to knowledge graphs. The proposed AIs-KG theory bridges formal concept analysis and knowledge graphs, enhancing knowledge completeness and feasibility.
Article
Physics, Multidisciplinary
Ting Qian, Yongwei Yang, Xiaoli He
Summary: This paper investigates the characteristics of formal context in three-way concept analysis, discussing the relationships between different types of intersectable contexts, and deriving conclusions based on isomorphic and anti-isomorphic relationships between concept lattices and OEOL.
Article
Computer Science, Artificial Intelligence
Guilong Liu, Yanbin Feng
Summary: This paper explores the application of knowledge granularity reduction algorithm in rough set theory and derives relevant algorithms based on a discernibility matrix to fill the gap in this field. In the end, a simple algorithm using binary integer programming is provided, and validated on six UCI datasets.
INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS
(2022)
Article
Computer Science, Artificial Intelligence
Yehai Xie, Xiuwei Gao
Summary: In this study, topological methods are used to investigate attribute reduction problems for relation decision systems. The notions of topological consistent and inconsistent relation decision systems are proposed, and the concepts of consistent topological reduction and general topological reduction are introduced. Reduction algorithms are developed for these two types of reductions, and experimental results on 11 UCI datasets demonstrate the effectiveness and practicability of the proposed algorithms.
INTERNATIONAL JOURNAL OF APPROXIMATE REASONING
(2023)
Article
Computer Science, Interdisciplinary Applications
Catherine Hurley
Summary: Formal concept analysis (FCA) is a mathematical framework based on logic and lattice theory to manage information through concepts and implications. The R package fcaR implements core FCA notions, extends to fuzzy datasets, and develops automated reasoning tools. It also interfaces with the arules package and is applied in designing a recommender system for neurological pathology diagnosis based on logic.
Article
Computer Science, Artificial Intelligence
Siyu Zhao, Jianjun Qi, Junan Li, Ling Wei
Summary: Reduction theory, specifically attribute reduction, is a significant topic in formal concept analysis. However, attribute reduction may lead to information loss. Concept reduction, as a new direction, avoids this issue and simplifies problem solving with formal concept analysis. This paper introduces the concept of representative concept matrix to visualize the connection between concepts and binary relations, and proposes methods and algorithms for concept reduction.
INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS
(2023)
Article
Mathematics
Maria Jose Benitez-Caballero, Jesus Medina, Eloisa Ramirez-Poussa
Summary: This paper demonstrates that one-sided concept lattices are specific instances of multi-adjoint concept lattices, and introduces a new attribute reduction mechanism in the one-sided framework.
Article
Mathematics
B. Srirekha, Shakeela Sathish, R. Narmada Devi, Miroslav Mahdal, Robert Cep, K. Elavarasan
Summary: This paper introduces an object ranking concept to define a consistency set and reduction of the attributes by structural features. An incomplete information system works on the three-way concepts using the SE-ISI Context. Granularity is emphasized with join (meet) irreducible sets using the object ranking concepts. A dual operator is defined based on the object ranking concepts and its properties and conditions are verified. This elaborates on the four kinds of reduction of the attributes.
Article
Computer Science, Artificial Intelligence
Zhong-Ling Li, Ju-Sheng Mi, Tao Zhang
Summary: Based on cognitive operators, this study examines the granular reduct of dynamic formal context. Using cognitive operators, the discernibility attribute sets in different states are determined when the objects and attributes increase in a non-decision formal context. The relation between the new reduct and the original reduct is established through Boolean reasoning when objects and attributes increase. Updated algorithms for the new granular reduct when objects and attributes are added are formulated.
INTERNATIONAL JOURNAL OF APPROXIMATE REASONING
(2023)
Article
Environmental Sciences
Jihong Chen, Hao Chen, Jia Shi, Tao Yan, Miao Gu, Xiutao Huang
Summary: Ship oil spill accidents have a prolonged duration, complex consequences, and pose a significant threat to the environment, economy, and society. This article uses accident databases and a formal concept analysis model to identify key factors such as improper operation, incomplete ship equipment, large tonnage, and poor navigation conditions. Corresponding improvement measures are proposed based on different causal rules of oil spills.
MARINE POLLUTION BULLETIN
(2023)
Article
Computer Science, Artificial Intelligence
Rongde Lin, Jinjin Li, Dongxiao Chen, Jianxin Huang, Yingsheng Chen
Summary: This paper introduces a new method for attribute reduction by utilizing parametric observation sets and parametric observational-consistency to improve efficiency. A discernibility matrix is developed to provide a way of attribute reduction. A recursive method for multiple observational parameters is proposed to construct multiple discernibility matrix gradually.
JOURNAL OF INTELLIGENT & FUZZY SYSTEMS
(2021)
Article
Computer Science, Artificial Intelligence
Lei Zhao, Ling-Xia Lu, Wei Yao
Summary: This paper introduces a concept of generalized three-way concept lattices for supporting the idea of three-way decisions. The generalized three-way operators are defined and their properties are studied. Two types of generalized three-way concept lattices are constructed based on these operators. The generalized three-way concept lattices provide a more general model compared to Wille's concept lattices and Qi-Wei-Yao's three-way concept lattices.
Article
Computer Science, Artificial Intelligence
Zhong Yuan, Hongmei Chen, Peng Xie, Pengfei Zhang, Jia Liu, Tianrui Li
Summary: This paper investigated attribute reduction methods in fuzzy rough set theory, comparing and analyzing three different types of reduction rules through experiments, which can retain fewer attributes while improving or maintaining the classification accuracy of a classifier. Furthermore, some new research directions were discussed.
APPLIED SOFT COMPUTING
(2021)
Article
Computer Science, Artificial Intelligence
Zhen Wang, Chengjun Shi, Ling Wei, Yiyu Yao
Summary: This paper proposes a unified tri-granularity model for attribute reduction in three-way concept lattices, which allows examining the concept lattice at different levels of granularity. It introduces definitions and methods for local granularity and elementary granularity attribute reduction and analyzes their relationship with global granularity. Two attribute reduction algorithms are designed and tested for their effectiveness.
KNOWLEDGE-BASED SYSTEMS
(2023)
