Article
Computer Science, Artificial Intelligence
Qian Hu, Keyun Qin, Han Yang, Binbin Xue
Summary: This paper focuses on the methods of rule acquisition and attribute reduction. It proposes a method to simplify the discernibility matrix and a heuristic approach to compute reductions. A novel rule acquisition algorithm for OW-decision rules is also presented. Experimental results show the effectiveness and efficiency of the proposed algorithms.
APPLIED INTELLIGENCE
(2023)
Article
Computer Science, Artificial Intelligence
Bryar A. Hassan, Tarik A. Rashid, Seyedali Mirjalili
Summary: It is beneficial to automate the process of deriving concept hierarchies from corpora since manual construction can be time-consuming and resource-intensive. This study proposes two frameworks: one to review the current process of deriving concept hierarchies from corpora utilizing FCA, and the other to reduce formal context ambiguity using an adaptive version of evolutionary clustering algorithm (ECA*). Experiments show that the adaptive ECA* outperforms other competitive techniques in concept lattice formation speed.
COMPLEX & INTELLIGENT SYSTEMS
(2021)
Article
Computer Science, Artificial Intelligence
Ju Huang, Yidong Lin, Jinjin Li
Summary: This paper discusses a method for rule reduction in formal contexts with mixed information. It presents a mixed decision rule based on a mixed concept lattice and introduces a novel approach for weak-basis from the viewpoint of granular computing to reduce the redundance of rules. The comparison of mixed decision rules and three-way decision rules is thoroughly discussed, and a case study is provided for illustration of the differences.
APPLIED INTELLIGENCE
(2023)
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
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
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
Computer Science, Artificial Intelligence
Li Zou, Ning Kang, Lu Che, Xin Liu
Summary: This approach uses linguistic-valued layered concept lattice and rule extraction algorithm with trust degree to handle linguistic uncertainty, simplify rule acquisition process, and obtain more compact linguistic-valued decision rules.
INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS
(2022)
Article
Computer Science, Artificial Intelligence
Yanhui Zhai, Nan Jia, Shaoxia Zhang, Deyu Li, Weihua Xu
Summary: This article discusses the application and interchangeability of decision implication inference rules, and proposes three inference methods. One of the methods, applying augmentation once and then applying CON-COMBINATION at most inverted right perpendicularlog(2) minverted left perpendicular times, is proven to be the most efficient.
INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS
(2022)
Article
Multidisciplinary Sciences
Liying Yang, Jinjin Li, Chengling Zhang, Yidong Lin
Summary: Knowledge Space Theory (KST) is a mathematical framework for assessing knowledge and learning in education, aimed at achieving all atoms and investigating the relationship between knowledge reduction and molecules in the knowledge space based on FCA.
Article
Computer Science, Artificial Intelligence
Mingxia Li, Kebing Chen, Baoxiang Liu
Summary: This paper proposes a method based on interval concept lattice theory to indirectly reflect the substitutability between products through association rule mining and rule optimization algorithm, providing some implications for pricing for suppliers in competitive supply chains.
JOURNAL OF INTELLIGENT & FUZZY SYSTEMS
(2022)
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
Physics, Multidisciplinary
Ola Hossjer, Daniel Andres Diaz-Pachon, J. Sunil Rao
Summary: This article introduces a mathematical framework that defines learning and knowledge acquisition using Bayesian probability theory. Learning occurs when an agent's belief in a true proposition increases or belief in a false proposition decreases. Knowledge acquisition requires learning for the right reason and estimation of true world parameters.
Article
Computer Science, Artificial Intelligence
Lubomir Antoni, M. Eugenia Cornejo, Jesus Medina, Eloisa Ramirez-Poussa
Summary: The article discusses the issue of reducing information in databases through formal concept analysis, focusing on multi-adjoint concept lattices in a fuzzy environment. Algorithms are introduced to discover information in relational systems, allowing for classification and creation of minimal attribute subsets that preserve the original knowledge system's information.
IEEE TRANSACTIONS ON FUZZY SYSTEMS
(2021)
Article
Computer Science, Artificial Intelligence
Han Yang, Keyun Qin, Qian Hu, Lei Yang
Summary: With the expansion of databases, the complexity of concept lattices increases rapidly. This paper proposes a neighborhood based concept lattice to compress formal concepts, and experimental results show its effectiveness.
APPLIED INTELLIGENCE
(2023)
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
Xiao Zhang, Changlin Mei, Degang Chen, Yanyan Yang, Jinhai Li
IEEE TRANSACTIONS ON FUZZY SYSTEMS
(2020)
Article
Computer Science, Artificial Intelligence
Zehua Jiang, Keyu Liu, Jingjing Song, Xibei Yang, Jinhai Li, Yuhua Qian
Summary: This paper proposes a method of crosswise computing reducts, which calculates multiple reducts by grouping and crosswise selecting, and designs an acceleration strategy. Experimental results show that the method can significantly reduce computation time, provide reducts with higher stability, and maintain classification performance.
APPLIED SOFT COMPUTING
(2021)
Article
Automation & Control Systems
Yong Shi, Yunlong Mi, Jinhai Li, Wenqi Liu
Summary: Concept-cognitive learning (CCL) is a new field that focuses on incremental concept learning and dynamic knowledge processing. The existing research aims to address the lack of generalization ability in CCL systems, and proposes a new CCLM model to naturally integrate new data for enhanced concept learning flexibility.
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
(2021)
Article
Computer Science, Artificial Intelligence
Bo Yang, Jinhai Li
Summary: The study shows that the relationship between the self-questioning dynamical evolutionary game model and Ising model is independent of network structure, and the dividing lines obtained are suitable for arbitrary networks. Nodes with large degree exhibit higher stability and robustness compared to those with small degree.
INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS
(2021)
Article
Automation & Control Systems
Yunlong Mi, Yong Shi, Jinhai Li, Wenqi Liu, Mengyu Yan
Summary: This article introduces a fuzzy-based concept learning model (FCLM) to address the limitations of standard concept learning algorithms in handling continuous data and ignoring object information. By utilizing concept hierarchical relations in concept lattices, the proposed model achieves state-of-the-art classification performance and demonstrates effectiveness in concept discovery.
IEEE TRANSACTIONS ON CYBERNETICS
(2022)
Article
Computer Science, Artificial Intelligence
Yingsheng Chen, Jinhai Li, Jinjin Li, Rongde Lin, Dongxiao Chen
Summary: This paper explores the issue of optimal scale selection in multi-scale decision information systems, emphasizing the dynamic changes and increasing amount of information in big data. It further investigates the change laws of optimal scale when adding an object, developing sufficient and necessary conditions for updating the optimal scale, making the theoretical study more complete.
INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS
(2022)
Article
Computer Science, Artificial Intelligence
YunLong Mi, Wenqi Liu, Yong Shi, Jinhai Li
Summary: In human concept learning, semi-supervised learning combines labeled and unlabeled data, and this approach needs to be redesigned for new data input. This study proposes a novel method for dynamic semi-supervised learning using concept spaces and structures, which mimics human cognitive processes.
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
(2022)
Article
Computer Science, Artificial Intelligence
Min Fan, Shan Luo, Jinhai Li
Summary: Knowledge discovery combined with network structure is an emerging field. This paper proposes a network formal context of three-way decision (NFC3WD) and studies network weaken-concepts and their corresponding sub-networks. Furthermore, the concept logic of the network and the properties of its operators are put forward, and rule extraction algorithms are designed. Moreover, these algorithms are applied to COVID-19 diagnosis examples, showing the superiority of the proposed method.
APPLIED INTELLIGENCE
(2023)
Article
Computer Science, Artificial Intelligence
Jiaojiao Niu, Degang Chen, Jinhai Li, Hui Wang
Summary: This article focuses on the learning of granular rules and proposes a novel fuzzy rule-based classification model. It improves the readability and efficiency through granular reducts and demonstrates its effectiveness with numerical experiments.
IEEE TRANSACTIONS ON FUZZY SYSTEMS
(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, Artificial Intelligence
Yingsheng Chen, Jinhai Li, Jinjin Li, Dongxiao Chen, Rongde Lin
Summary: The multi-scale decision information system is a typical granular computing model that requires consideration of uncertainty and optimal scale selection. This study investigates the updating law of the local optimal scale under the condition of dynamic object increase and proposes a method to define and update the local optimal scale based on the uncertainty of decision classes. Experimental results demonstrate the correctness and effectiveness of the proposed method in calculating the local optimal scale.
INTERNATIONAL JOURNAL OF APPROXIMATE REASONING
(2023)
Article
Computer Science, Artificial Intelligence
Jinhai Li, Ye Feng
Summary: The problem of optimal scale selection for multi-scale decision information systems is an important issue in granular computing research, particularly in the context of dynamic data updates. Scholars have focused on determining the changes in the optimal scale for dynamic data, especially with regards to newly added objects. Existing studies have only addressed the conditions for the optimal scale becoming smaller. Therefore, it is necessary to explore the conditions for the optimal scale remaining unchanged or becoming larger. This paper uses three-way decision theory to study this problem and provides a solution to finding the changing laws of the optimal scale for object updating in multi-scale decision information systems.
INTERNATIONAL JOURNAL OF APPROXIMATE REASONING
(2023)
Article
Computer Science, Artificial Intelligence
Xiao Zhang, Changlin Mei, Jinhai Li, Yanyan Yang, Ting Qian
Summary: Data reduction is a crucial technique for preprocessing data, aiming to select the most representative information to reduce the original data. The development of data reduction techniques for large-scale or huge data has gained significant attention. This article investigates the simultaneous selection of feature and instance in data reduction using fuzzy rough sets, presenting an integrated approach called BSFRS. Experimental results demonstrate the effectiveness of BSFRS in data reduction.
IEEE TRANSACTIONS ON FUZZY SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Keyi Guo, Jinhai Li, Xiao Zhang
Summary: The concept-cognitive learning (CCL) process is a specific step in simulating human brain learning. Different CCL models result in different concept learning outcomes. The existing CCL model based on granule approximations lacks accuracy and cannot guarantee consistency. We propose a new CCL method with hybrid lattice structure to improve learning accuracy and develop algorithms for various clue scenarios. Additionally, we introduce a non-logical associative CCL method to handle cases with unreasonable learning outcomes. Experimental results demonstrate the effectiveness of these methods.
INTERNATIONAL JOURNAL OF APPROXIMATE REASONING
(2023)
Article
Computer Science, Artificial Intelligence
Zhiming Liu, Jinhai Li, Xiao Zhang, Xi-Zhao Wang
Summary: This article proposes a novel concept-cognitive learning method called SI2CCLM, which addresses the dependency on attribute order issue in existing methods by adopting a stochastic strategy independent of attribute order. A classification algorithm based on SI2CCLM is developed, and the analysis of the algorithm's parameters and convergence is conducted.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2023)
Article
Computer Science, Information Systems
Xia Liang, Jie Guo, Peide Liu
Summary: This paper investigates a novel consensus model based on social networks to manage manipulative and overconfident behaviors in large-scale group decision-making. By proposing a novel clustering model and improved methods, the consensus reaching is effectively facilitated. The feedback mechanism and management approach are employed to handle decision makers' behaviors. Simulation experiments and comparative analysis demonstrate the effectiveness of the model.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Xiang Li, Haiwang Guo, Xinyang Deng, Wen Jiang
Summary: This paper proposes a method based on class gradient networks for generating high-quality adversarial samples. By introducing a high-level class gradient matrix and combining classification loss and perturbation loss, the method demonstrates superiority in the transferability of adversarial samples on targeted attacks.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Lingyun Lu, Bang Wang, Zizhuo Zhang, Shenghao Liu
Summary: Many recommendation algorithms only rely on implicit feedbacks due to privacy concerns. However, the encoding of interaction types is often ignored. This paper proposes a relation-aware neural model that classifies implicit feedbacks by encoding edges, thereby enhancing recommendation performance.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Jaehong Yu, Hyungrok Do
Summary: This study discusses unsupervised anomaly detection using one-class classification, which determines whether a new instance belongs to the target class by constructing a decision boundary. The proposed method uses a proximity-based density description and a regularized reconstruction algorithm to overcome the limitations of existing one-class classification methods. Experimental results demonstrate the superior performance of the proposed algorithm.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Hui Tu, Shifei Ding, Xiao Xu, Haiwei Hou, Chao Li, Ling Ding
Summary: Border-Peeling algorithm is a density-based clustering algorithm, but its complexity and issues on unbalanced datasets restrict its application. This paper proposes a non-iterative border-peeling clustering algorithm, which improves the clustering performance by distinguishing and associating core points and border points.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Long Tang, Pan Zhao, Zhigeng Pan, Xingxing Duan, Panos M. Pardalos
Summary: In this work, a two-stage denoising framework (TSDF) is proposed for zero-shot learning (ZSL) to address the issue of noisy labels. The framework includes a tailored loss function to remove suspected noisy-label instances and a ramp-style loss function to reduce the negative impact of remaining noisy labels. In addition, a dynamic screening strategy (DSS) is developed to efficiently handle the nonconvexity of the ramp-style loss.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Raghunathan Krishankumar, Sundararajan Dhruva, Kattur S. Ravichandran, Samarjit Kar
Summary: Health 4.0 is gaining global attention for better healthcare through digital technologies. This study proposes a new decision-making framework for selecting viable blockchain service providers in the Internet of Medical Things (IoMT). The framework addresses the limitations in previous studies and demonstrates its applicability in the Indian healthcare sector. The results show the top ranking BSPs, the importance of various criteria, and the effectiveness of the developed model.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Tao Tan, Hong Xie, Liang Feng
Summary: This paper proposes a heterogeneous update idea and designs HetUp Q-learning algorithm to enlarge the normalized gap by overestimating the Q-value corresponding to the optimal action and underestimating the Q-value corresponding to the other actions. To address the limitation, a softmax strategy is applied to estimate the optimal action, resulting in HetUpSoft Q-learning and HetUpSoft DQN. Extensive experimental results show significant improvements over SOTA baselines.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Chao Yang, Xianzhi Wang, Lina Yao, Guodong Long, Guandong Xu
Summary: This paper proposes a dynamic transformer-based architecture called Dyformer for multivariate time series classification. Dyformer captures multi-scale features through hierarchical pooling and adaptive learning strategies, and improves model performance by introducing feature-map-wise attention mechanisms and a joint loss function.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Xiguang Li, Baolu Feng, Yunhe Sun, Ammar Hawbani, Saeed Hammod Alsamhi, Liang Zhao
Summary: This paper proposes an enhanced scatter search strategy, using opposition-based learning, to solve the problem of automated test case generation based on path coverage (ATCG-PC). The proposed ESSENT algorithm selects the path with the lowest path entropy among the uncovered paths as the target path and generates new test cases to cover the target path by modifying the dimensions of existing test cases. Experimental results show that the ESSENT algorithm outperforms other state-of-the-art algorithms, achieving maximum path coverage with fewer test cases.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Shirin Dabbaghi Varnosfaderani, Piotr Kasprzak, Aytaj Badirova, Ralph Krimmel, Christof Pohl, Ramin Yahyapour
Summary: Linking digital accounts belonging to the same user is crucial for security, user satisfaction, and next-generation service development. However, research on account linkage is mainly focused on social networks, and there is a lack of studies in other domains. To address this, we propose SmartSSO, a framework that automates the account linkage process by analyzing user routines and behavior during login processes. Our experiments on a large dataset show that SmartSSO achieves over 98% accuracy in hit-precision.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Renchao Wu, Jianjun He, Xin Li, Zuguo Chen
Summary: This paper proposes a memetic algorithm with fuzzy-based population control (MA-FPC) to solve the joint order batching and picker routing problem (JOBPRP). The algorithm incorporates batch exchange crossover and a two-level local improvement procedure. Experimental results show that MA-FPC outperforms existing algorithms in terms of solution quality.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Guoxiang Zhong, Fagui Liu, Jun Jiang, Bin Wang, C. L. Philip Chen
Summary: In this study, we propose the AMFormer framework to address the problem of mixed normal and anomaly samples in deep unsupervised time-series anomaly detection. By refining the one-class representation and introducing the masked operation mechanism and cost sensitive learning theory, our approach significantly improves anomaly detection performance.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Jin Zhou, Kang Zhou, Gexiang Zhang, Ferrante Neri, Wangyang Shen, Weiping Jin
Summary: In this paper, the authors focus on the issue of multi-objective optimisation problems with redundant variables and indefinite objective functions (MOPRVIF) in practical problem-solving. They propose a dual data-driven method for solving this problem, which consists of eliminating redundant variables, constructing objective functions, selecting evolution operators, and using a multi-objective evolutionary algorithm. The experiments conducted on two different problem domains demonstrate the effectiveness, practicality, and scalability of the proposed method.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Georgios Charizanos, Haydar Demirhan, Duygu Icen
Summary: This article proposes a new fuzzy logistic regression framework that addresses the problems of separation and imbalance while maintaining the interpretability of classical logistic regression. By fuzzifying binary variables and classifying subjects based on a fuzzy threshold, the framework demonstrates superior performance on imbalanced datasets.
INFORMATION SCIENCES
(2024)
