Article
Computer Science, Artificial Intelligence
Prem Kumar Singh
Summary: The study of bipolar fuzzy concept lattice is important for analyzing uncertainty in soft data sets and can help in making more informed decisions. By measuring randomness in bipolar fuzzy concepts using Shannon entropy and determining importance through computed weights, the study aims to provide valuable insights for decision-making processes.
Article
Mathematics, Applied
Imran Ali, Yongming Li, Witold Pedrycz
Summary: This study focuses on analyzing the spatial and temporal aspects of events data using intuitionistic fuzzy (IF) datasets and the granular computing (GrC) paradigm. The goal is to discover the periodicity of events and predict future occurrences.
Article
Computer Science, Artificial Intelligence
Witold Pedrycz
Summary: This article proposes a data compression method based on granular computing, which emphasizes the emergence of information granules during the compression process. A two-phase design environment is established with the algorithmic layer of fuzzy clustering and the principle of justifiable granularity. The method's performance is evaluated using reconstruction error and granular reconstruction error criteria, and experimental studies are conducted to analyze the obtained results.
IEEE TRANSACTIONS ON FUZZY SYSTEMS
(2023)
Article
Automation & Control Systems
Yanhui Zhai, Tao Wang, Deyu Li
Summary: Formal concept analysis is an effective tool for data analysis and visualization using concept lattice. This paper introduces the tolerance threshold to the variable threshold concept lattice, forming the Robust variable threshold fuzzy Concept Lattice (RobCL). RobCL has incremental characteristics and can model the incremental cognitive process, distinguishing it from other concept lattice models. A comparative study shows that variable threshold concept lattice is a special case of RobCL.
INTERNATIONAL JOURNAL OF FUZZY SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Meng Hu, Eric C. C. Tsang, Yanting Guo, Qingshuo Zhang, Degang Chen, Weihua Xu
Summary: The study focuses on the mechanism of interval-valued CCL, proposing dual cognitive operators and interval-valued information granules as the foundation of concept learning, and establishing algorithms for automatic concept learning.
INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS
(2022)
Article
Computer Science, Artificial Intelligence
Stefania Boffa, Petra Murinova, Vilem Novak
Summary: This article integrates Relational Concept Analysis with fuzzy logic to explore multi-relational datasets containing vague information, aiming to extract fuzzy concept lattices from data organized as fuzzy formal contexts and relations between different types of objects by using fuzzy FCA techniques and scaling quantifiers. The main contribution lies in introducing and studying fuzzy scaling quantifiers based on evaluative linguistic expression.
KNOWLEDGE-BASED SYSTEMS
(2021)
Article
Computer Science, Artificial Intelligence
Tinghui Ouyang
Summary: In this paper, a new rule-based modeling approach is proposed to analyze the dynamic behaviors of complex systems in the era of big data. This approach incorporates structural information mining and granular computing, and uses DBSCAN and granular fuzzy intervals to reflect system behaviors on uncertainty. Experimental analysis demonstrates the superiority of the proposed approach in various design scenarios.
APPLIED SOFT COMPUTING
(2022)
Article
Computer Science, Artificial Intelligence
Huilai Zhi, Jinhai Li, Yinan Li
Summary: This study proposes a mathematical framework for multilevel conflict analysis from an outsider's perspective. By utilizing fuzzy formal concept analysis, the uncertainties of two sides can be managed simultaneously. Experimental results show that managing uncertainties can lead to more conflict resolutions, and increasing analysis levels helps achieve more economical conflict resolutions.
IEEE TRANSACTIONS ON FUZZY SYSTEMS
(2022)
Article
Computer Science, Information Systems
M. Eugenia Cornejo, Jesus Medina, Eloisa Ramirez-Poussa, Clemente Rubio-Manzano
Summary: This paper studies an inference method related to the qualitative preference levels considered in the applications by non-expert users of the FCA framework.
INFORMATION SCIENCES
(2023)
Article
Mathematics, Applied
M. Eugenia Cornejo, Juan Carlos Diaz-Moreno, Jesus Medina
Summary: Datasets often contain imprecise data or noise, which can lead to unexpected results in mappings. One way to minimize the impact of noise on the final results is to use generalized quantifiers. This paper presents four types of generalized quantifiers based on adjoint triples, which offer a more flexible framework compared to current approaches. The properties and characteristics of these quantifiers are studied, and their application in formal concept analysis is demonstrated.
JOURNAL OF COMPUTATIONAL AND APPLIED MATHEMATICS
(2022)
Article
Computer Science, Artificial Intelligence
Zengtai Gong, Jing Zhang
Summary: This paper proposes a graph-theoretic-based method to compute granular reducts in the formal fuzzy context. By introducing the induced graph of the granular discernibility matrix and showing the equivalence between the minimal vertex cover of the induced graph and the reduction of the FFC, the problem of reduction is transformed into finding the minimal vertex cover of a graph. Experimental results demonstrate that the proposed method performs well in terms of time complexity and running time.
ARTIFICIAL INTELLIGENCE REVIEW
(2023)
Article
Computer Science, Information Systems
Chang Liu, Zhong Yuan, Baiyang Chen, Hongmei Chen, Dezhong Peng
Summary: This study proposes an anomaly detection method based on fuzzy granules, which effectively handles fuzzy or uncertain information by using the fuzzy information granulation model. The method gradually develops a fuzzy granular anomaly detection method using the anomaly score calculation for each sample.
INFORMATION SCIENCES
(2023)
Article
Computer Science, Artificial Intelligence
Yan Li, Xingchen Hu, Witold Pedrycz, Fangjie Yang, Zhong Liu
Summary: In this study, we propose a novel approach for selective sampling and mapping data reduction to address the challenges in dealing with multivariable and large-scale data. The approach focuses on reducing data variables and samples while preserving the structural characteristics of the original data. A multivariable data-driven fuzzy rule-based model is then developed based on the processed data. Experimental studies demonstrate that the proposed method outperforms existing regression algorithms in terms of effectiveness and efficiency.
APPLIED SOFT COMPUTING
(2023)
Article
Computer Science, Theory & Methods
Yumin Chen, Shunzhi Zhu, Wei Li, Nan Qin
Summary: The study proposes a fuzzy granular convolutional classifier, which extracts features and optimizes weights through fuzzy granulation and convolutional operations, ultimately achieving better classification performance.
FUZZY SETS AND SYSTEMS
(2022)
Article
Business
Giuseppe Fenza, Mariacristina Gallo, Vincenzo Loia, Alessandra Petrone, Claudio Stanzione
Summary: Concept drift refers to the unpredictable changes in the underlying distribution of streaming data over time. Detecting, interpreting, and adapting to concept drift is crucial in concept drift research. It is found that machine learning in a concept drift environment produces poor results without handling drift. This study proposes a concept drift detection index based on Fuzzy Formal Concept Analysis theory to predict when the performance of a machine learning model for text-stream classifiers is low. Experimental results show a significant correlation between the proposed index and the accuracy of Random Forest, Naive Bayes, and Passive Aggressive models, suggesting that the index can prevent incorrect classifications and aid in retraining decisions.
TECHNOLOGICAL FORECASTING AND SOCIAL CHANGE
(2023)
Article
Computer Science, Artificial Intelligence
Prem Kumar Singh
NEURAL COMPUTING & APPLICATIONS
(2020)
Article
Computer Science, Artificial Intelligence
Prem Kumar Singh
Summary: The paper addresses the representation and approximation of uncertainty in fuzzy attributes, introducing a formal context and concept lattice based on cubic set. Results are compared with existing approaches through an illustrative example.
Article
Computer Science, Interdisciplinary Applications
Prem Kumar Singh
Summary: In recent years, various attempts have been made to measure the research outcome and impact of authors or institutes. This paper introduces a new metric called "t-index" to address the challenge of evaluating performance when publications are spread across different domains.
Article
Computer Science, Artificial Intelligence
Prem Kumar Singh
Summary: Complex fuzzy concept lattice can represent uncertainty and fluctuation, and provide a method for pattern discovery in decision making. The issue of generating numerous complex fuzzy concepts is addressed by proposing a method based on maximal acceptance. The weight of concepts is calculated to control information pollution.
NEURAL PROCESSING LETTERS
(2022)
Article
Computer Science, Artificial Intelligence
Prem Kumar Singh
Summary: Characterization of domain experts and document publication is a crucial task due to uncertainty and randomness. This paper proposes two methods using single-valued neutrosophic set and Shannon entropy to address the uncertainty and randomness in document publication in a given field.
ARTIFICIAL INTELLIGENCE REVIEW
(2023)
Article
Computer Science, Artificial Intelligence
Prem Kumar Singh
Summary: The study of bipolar fuzzy concept lattice is important for analyzing uncertainty in soft data sets and can help in making more informed decisions. By measuring randomness in bipolar fuzzy concepts using Shannon entropy and determining importance through computed weights, the study aims to provide valuable insights for decision-making processes.
Article
Computer Science, Artificial Intelligence
Prem Kumar Singh
Summary: This paper introduces a method to address the issue of dealing with a large number of bipolar fuzzy concepts in bipolar fuzzy concept lattice, and proposes a measurement method for bipolar fuzzy attribute implications using accuracy function with an illustrative example.
QUANTUM MACHINE INTELLIGENCE
(2022)
Article
Computer Science, Artificial Intelligence
Prem Kumar Singh
Summary: The research focuses on addressing uncertainty and dealing with fuzzy data beyond three-way fuzzy space, introducing a method connecting bipolar fuzzy set and multi-fuzzy set, and providing graphical structure visualization of bipolar multi-fuzzy set. Additionally, a method to refine the bipolar multi-fuzzy context is proposed.
GRANULAR COMPUTING
(2022)
Article
Computer Science, Artificial Intelligence
Prem Kumar Singh
Summary: The m-polar fuzzy graph representation of concept lattice provides a way to handle periodic changes, such as reviewer opinions towards a manuscript, in a given phase of time. Dealing with complex multi-fuzzy attributes is crucial for data analytic researchers, and the introduction of calculus of complex multi-fuzzy set and its granulation helps in processing and analyzing the information effectively.
GRANULAR COMPUTING
(2021)
Article
Computer Science, Artificial Intelligence
Prem Kumar Singh
GRANULAR COMPUTING
(2020)
Article
Computer Science, Artificial Intelligence
Prem Kumar Singh
GRANULAR COMPUTING
(2020)
Article
Computer Science, Artificial Intelligence
Said Broumi, Mullai Murugappan, Mohamed Talea, Assia Bakali, Florentin Smarandache, Prem Kumar Singh, Arindam Dey
NEUTROSOPHIC SETS AND SYSTEMS
(2019)
Article
Computer Science, Artificial Intelligence
Prem Kumar Singh
PROGRESS IN ARTIFICIAL INTELLIGENCE
(2019)
Article
Computer Science, Artificial Intelligence
Said Broumi, Mohamed Talea, Assia Bakali, Prem Kumar Singh, Florentin Smarandache
NEUTROSOPHIC SETS AND SYSTEMS
(2019)
Article
Computer Science, Artificial Intelligence
Said Broumi, Mohamed Talea, Assia Bakali, Florentin Smarandache, Prem Kumar Singh, Mullai Murugappan, V. Venkateswara Rao
NEUTROSOPHIC SETS AND SYSTEMS
(2019)
