Article
Computer Science, Artificial Intelligence
Wenbin Chen, Qinghua Zhang, Yongyang Dai
Summary: This study proposes a new sequential multi-class three-way decision model by considering the granular structure of the sequential process. The model defines decision cost, calculates attribute sequence, and the experimental results demonstrate its advantage in decision cost.
INTERNATIONAL JOURNAL OF APPROXIMATE REASONING
(2022)
Article
Computer Science, Information Systems
Yi Xu, Baofeng Li
Summary: This paper discusses the importance of multiview and multilevel in granular computing, and introduces a new partition order product space model. It proposes search algorithms and fusion strategies for solving three-way decisions from multiple views and multiple levels.
INFORMATION SCIENCES
(2022)
Article
Computer Science, Information Systems
Yongjing Zhang, Guannan Li, Wangchen Dai, Chengxin Hong, Jin Qian, Zhaoyang Han
Summary: In the context of IoT, decision models and GRC can be used for data processing. By incorporating device-free sensor data into decision models, IoT data can be processed more efficiently and accurate location positioning can be achieved.
COMPUTER COMMUNICATIONS
(2023)
Article
Computer Science, Artificial Intelligence
Jin Qian, Xing Han, Ying Yu, Caihui Liu, Jiamao Yu
Summary: The fuzzy decision-theoretic rough set method simplifies decision-making complexity and improves its scientific and rational aspects. However, it faces challenges such as insufficient decision information, single perspective, and high decision-making costs when dealing with dynamic, complex, and uncertain problems.
APPLIED SOFT COMPUTING
(2023)
Article
Computer Science, Information Systems
A. Savchenko
Summary: A novel image recognition algorithm based on sequential three-way decisions is introduced to speed up the inference in a convolutional neural network. This approach does not require a special training procedure for neural networks and can be used with arbitrary architectures, demonstrating a reduction in running time of up to 40% with a controlled decrease in accuracy when tested on several datasets and neural architectures.
INFORMATION SCIENCES
(2021)
Article
Computer Science, Artificial Intelligence
Xiaoqing Ye, Dun Liu
Summary: This paper proposes a novel interpretable sequential three-way recommendation strategy, CTR-CS3WR, which introduces collaborative topic regression and three novel granulation methods. Extensive experiments on two CiteUlike datasets confirm the effectiveness of the proposed strategy.
EXPERT SYSTEMS WITH APPLICATIONS
(2021)
Article
Computer Science, Artificial Intelligence
Xiaoqing Ye, Dun Liu
Summary: This paper proposes a novel interpretable sequential three-way recommendation strategy, CTR-based cost-sensitive sequential three-way recommendation (CTR-CS3WR), to achieve multilevel recommendation by introducing collaborative topic regression (CTR) and designing three novel granulation methods. Extensive experiments on two CiteUlike datasets confirm the effectiveness of the proposed strategy.
EXPERT SYSTEMS WITH APPLICATIONS
(2021)
Article
Computer Science, Artificial Intelligence
Xiaoqing Ye, Dun Liu
Summary: This paper proposes a novel interpretable sequential three-way recommendation strategy, CTR-CS3WR, which introduces collaborative topic regression and designs three granulation methods to achieve multilevel characteristics of recommendation information and interpretability of recommendation results. Experimental results validate the effectiveness of the proposed strategy.
EXPERT SYSTEMS WITH APPLICATIONS
(2021)
Article
Computer Science, Artificial Intelligence
Qian Wang, Yan Wan, Feng Feng
Summary: This study proposes a method for human-machine collaborative scoring of subjective assignments using artificial intelligence and natural language processing technology. The proposed method outperforms seven baseline models in terms of execution efficiency and reduces human workload.
EXPERT SYSTEMS WITH APPLICATIONS
(2023)
Article
Computer Science, Information Systems
Youxi Wu, Shuhui Cheng, Yan Li, Rongjie Lv, Fan Min
Summary: This paper proposes a SFNN model based on sequential three-way decisions, which dynamically learns the number of hidden layer nodes and sets sequential threshold parameters to enhance the performance of networks on structured datasets.
INFORMATION SCIENCES
(2023)
Article
Computer Science, Artificial Intelligence
Tianna Zhao, Yuanjian Zhang, Duoqian Miao, Hongyun Zhang
Summary: Multi-label classification is challenging due to imbalanced class distribution and uncertain label correlation. This study proposes a multi-granular label information system, MGT-LEML, to analyze coarse-grained logical labels and reduce classification error for uncertain instances. Experimental results demonstrate the superiority of MGT-LEML algorithm over state-of-the-art methods, achieving significant improvements in various metrics.
INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS
(2023)
Article
Computer Science, Information Systems
Wenjie Wang, Jianming Zhan, Chao Zhang
Summary: Multi-attribute decision making (MADM) is a crucial part of modern decision sciences, with three-way decisions (3WD) being able to reduce decision risks and improve accuracy compared to traditional two-way decisions (2WD). This paper presents a new 3WD-MADM model based on probabilistic dominance relations, and validates its effectiveness through comparative and experimental analyses.
INFORMATION SCIENCES
(2021)
Article
Computer Science, Information Systems
Chengyong Jin, Bao Qing Hu
Summary: This paper introduces the concept of hesitant sets to unify different types of fuzzy sets. It discusses the construction of decision evaluation functions and three-way decisions based on hesitant sets in three-way decision spaces. The paper presents methods for constructing decision evaluation functions in semi-three-way and quasi-three-way decision spaces, as well as transformation methods to three-way decision spaces. The importance of these methods is supported by numerous examples.
INFORMATION SCIENCES
(2023)
Article
Computer Science, Information Systems
Xuanqian Chen, Bing Huang, Tianxing Wang
Summary: Optimal scale selection is widely studied to extract scale rules from multi-scale decision tables. However, this study focuses on generating an optimal scale and extracting scale rules from single-scale decision tables using sequential three-way decision (S3WD). By determining the importance of each attribute in a single-scale two-class dominance decision table and constructing object granules, we are able to classify objects and extract scale rules. Experiments on UCI datasets demonstrate the effectiveness of our method. Overall, our work provides a method for generating an optimal scale in single-scale decision tables and extracting scale rules.
INFORMATION SCIENCES
(2023)
Article
Computer Science, Interdisciplinary Applications
Junliang Du, Sifeng Liu, Yong Liu
Summary: This paper proposes a novel grey multi-criteria three-way decisions model based on grey incidence analysis and TOPSIS, which integrates three-way decision with grey system theory and expands totally ordered set evaluation-based three-way decisions. By considering multi-criteria information system and multi-criteria loss function matrix, the model calculates criteria weights and designs weighted aggregation operators for loss function with interval grey numbers, leading to grey multi-criteria three-way decision rules. The applicability and interpretability of the proposed model are validated through illustrative examples of complex equipment supplier selection and movie recommendation.
COMPUTERS & INDUSTRIAL ENGINEERING
(2021)
Review
Computer Science, Artificial Intelligence
Fei Teng, Yiming Liu, Tianrui Li, Yi Zhang, Shuangqing Li, Yue Zhao
Summary: The International Classification of Diseases (ICD) is widely used for categorizing physical conditions. Manual ICD coding is time-consuming and prone to errors. Therefore, researchers are focusing on using deep neural networks for ICD automatic coding.
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
(2023)
Article
Computer Science, Theory & Methods
Tengyu Yin, Hongmei Chen, Tianrui Li, Zhong Yuan, Chuan Luo
Summary: This paper investigates the use of soft labels for label enhancement in multilabel feature selection. By constructing a robust fuzzy neighborhood and utilizing a label enhancement strategy, the accuracy of feature selection in multilabel data can be improved. The research results demonstrate the good performance of this method in terms of classification performance and anti-noise ability.
FUZZY SETS AND SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Jihong Wan, Hongmei Chen, Tianrui Li, Binbin Sang, Zhong Yuan
Summary: This study proposes a method for feature selection in data with uncertainty, fuzziness, and noise. A robust fuzzy rough set model is constructed to enhance the robustness and antinoise ability. Uncertainty measures are defined to analyze the interactivity and redundancy of features. Experimental results demonstrate the significance of the proposed method.
IEEE TRANSACTIONS ON FUZZY SYSTEMS
(2023)
Article
Automation & Control Systems
Jihong Wan, Hongmei Chen, Tianrui Li, Zhong Yuan, Jia Liu, Wei Huang
Summary: This paper proposes a novel interactive and complementary feature selection approach based on a fuzzy multineighborhood rough set model. The approach effectively improves the classification performance of feature subsets while reducing the dimension of feature space.
IEEE TRANSACTIONS ON CYBERNETICS
(2023)
Article
Computer Science, Artificial Intelligence
Yuxin Zhong, Hongjun Wang, Wenlu Yang, Luqing Wang, Tianrui Li
Summary: This paper proposes a multi-objective genetic model for co-clustering ensemble (GMCCE) that combines fuzzy clustering and hard co-clustering. The model is solved using genetic algorithms, and experiments demonstrate its superior performance compared to other algorithms.
APPLIED SOFT COMPUTING
(2023)
Article
Computer Science, Artificial Intelligence
Xiaoling Yang, Hongmei Chen, Hao Wang, Tianrui Li, Zeng Yu, Zhihong Wang, Chuan Luo
Summary: This article proposes a local density-based fuzzy rough set (LDFRS) model to handle noisy data, and introduces mutual information to evaluate uncertainty in data. Furthermore, a joint feature evaluation function on the indispensability and relevance of features is constructed. Based on these works, a fuzzy rough feature selection algorithm is developed, and experimental results demonstrate the robustness and effectiveness of the proposed model.
IEEE TRANSACTIONS ON FUZZY SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Wei Huang, Jia Liu, Tianrui Li, Tianqiang Huang, Shenggong Ji, Jihong Wan
Summary: This paper proposes a federated learning framework based on deep reinforcement learning model for daily schedule recommendation. The model demonstrates superior performance in experiments with the help of curriculum learning and similarity aggregation algorithm.
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
(2023)
Article
Computer Science, Artificial Intelligence
Qiang Gao, Wei Wang, Li Huang, Xin Yang, Tianrui Li, Hamido Fujita
Summary: Trip recommendation is a popular and significant location-aware service that can help visitors make more accurate travel plans. However, previous studies face challenges such as capturing heterogeneous interactions, dealing with data sparsity, and considering contextual facts. To address these challenges, this work proposes a novel framework called GraphTrip, which utilizes spatial-temporal graph representation learning, dual-grained human mobility learning, and explicit information fusion to improve trip inference performance. The experimental results demonstrate promising gains against cutting-edge baselines.
INFORMATION FUSION
(2023)
Article
Computer Science, Information Systems
Wei Huang, Jia Liu, Tianrui Li, Shenggong Ji, Dexian Wang, Tianqiang Huang
Summary: In this paper, a model called FedCKE for cross-domain knowledge graph embedding in federated learning is proposed to securely handle embedding of entity/relation between different domains with non-shared data. An inter-domain encrypted entity/relation alignment method is presented using the encrypted sample alignment method in vertical federated learning, which can obtain entity/relation intersections between different domains without revealing any triples structure and additional entities/relations. On the server, the same entity/relation embeddings are aggregated using association and the parameter-secure aggregation method in horizontal federated learning. Experimental results demonstrate that the proposed FedCKE model can enhance the embedding of different clients (domains).
IEEE TRANSACTIONS ON BIG DATA
(2023)
Article
Engineering, Electrical & Electronic
Ghufran Ahmad Khan, Jie Hu, Tianrui Li, Bassoma Diallo, Hongjun Wang
Summary: This article introduces a novel multi-view clustering approach that deploys concept factorization to preserve the intrinsic geometry of the data and consensus representation. It also adopts correlation constraint and smooth regularization term to address the issues of overfitting redundant features and ignoring the correlation among multiple views. The proposed algorithm outperforms state-of-the-art approaches in clustering performance, as demonstrated by extensive experiments on benchmark datasets.
DIGITAL SIGNAL PROCESSING
(2023)
Article
Computer Science, Information Systems
Tingquan Deng, Ge Yang, Yang Huang, Ming Yang, Hamido Fujita
Summary: Sparse subspace clustering (SSC) focuses on revealing data distribution from algebraic perspectives and has been widely applied to high-dimensional data. The key to SSC is to learn the sparsest representation and derive an adjacency graph. From the perspective of granular computing, the notion of scored nearest neighborhoods is introduced to develop multi-granularity neighborhoods of samples. The multi-granularity representation of samples is integrated with SSC to collaboratively learn the sparse representation, and an adaptive multi-granularity sparse subspace clustering model (AMGSSC) is proposed.
INFORMATION SCIENCES
(2023)
Article
Computer Science, Artificial Intelligence
Baihong Han, Xiaoyan Jiang, Zhijun Fang, Hamido Fujita, Yongbin Gao
Summary: This paper proposes a novel automatic prompt generation method called F-SCP, which focuses on generating accurate prompts for low-accuracy classes and similar classes. Experimental results show that our approach outperforms state-of-the-art methods on six multi-domain datasets.
PATTERN RECOGNITION
(2024)
Article
Computer Science, Artificial Intelligence
Muhammad Hafeez Javed, Tianrui Li, Zeng Yu, Ayyaz Hussain, Taha M. Rajeh, Fan Zhang
Summary: This article presents a robust pyramidal frame prediction architecture and an efficient anomaly detection mechanism for video anomaly detection in intelligent surveillance systems. The architecture incorporates localized predictors, bidirectional sampling techniques, and an innovative loss function to handle anomalies as a regression problem, enabling a detailed understanding of anomalous events at a granular level.
KNOWLEDGE-BASED SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Dalibor Cimr, Hamido Fujita, Damian Busovsky, Richard Cimler
Summary: Automated computer-aided diagnosis (CAD) is an effective method for early detection of health issues, and this study proposes a CAD system for seizure detection with optimized complexity. The results demonstrate the effectiveness of the proposed model in providing decision support in both clinical and home environments.
INFORMATION FUSION
(2024)
Article
Computer Science, Artificial Intelligence
Chuan Luo, Sizhao Wang, Tianrui Li, Hongmei Chen, Jiancheng Lv, Zhang Yi
Summary: This article proposes a novel global search method for numerical feature selection, RH-BPSO, based on the hybridization of the rough hypercuboid approach and binary particle swarm optimization (BPSO) algorithm. Parallelization approaches for large-scale datasets are also presented by decomposing and recombining hypercuboid equivalence partition matrix. The experimental results indicate that RH-BPSO outperforms other feature selection algorithms in terms of classification accuracy, the cardinality of the selected feature subset, and execution efficiency. Moreover, the distributed meta-heuristic optimized rough hypercuboid feature selection algorithm, DiRH-BPSO, is significantly faster than its sequential counterpart and can handle large-scale feature selection tasks on distributed-memory multicore clusters.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Timotheus Kampik, Kristijonas Cyras, Jose Ruiz Alarcon
Summary: This paper presents a formal approach to explaining changes in inference in Quantitative Bipolar Argumentation Frameworks (QBAFs). The approach traces the causes of strength inconsistencies and provides explanations for them.
INTERNATIONAL JOURNAL OF APPROXIMATE REASONING
(2024)
Article
Computer Science, Artificial Intelligence
Xiangnan Zhou, Longchun Wang, Qingguo Li
Summary: This paper aims to establish a closer connection between domain theory and Formal Concept Analysis (FCA) by introducing the concept of an optimized concept for a formal context. With the utilization of optimized concepts, it is demonstrated that the class of formal contexts directly corresponds to algebraic domains. Additionally, two subclasses of formal contexts are identified to characterize algebraic L-domains and Scott domains. An application is presented to address the open problem of reconstructing bounded complete continuous domains using attribute continuous contexts, and the presentation of algebraic domains is extended to a categorical equivalence.
INTERNATIONAL JOURNAL OF APPROXIMATE REASONING
(2024)
Article
Computer Science, Artificial Intelligence
Sihan Wang, Zhong Yuan, Chuan Luo, Hongmei Chen, Dezhong Peng
Summary: Anomaly detection is widely used in various fields, but most current methods only work for specific data and ignore uncertain information such as fuzziness. This paper proposes an anomaly detection algorithm based on fuzzy rough entropy, which effectively addresses the similarity between high-dimensional objects using distance and correlation measures. The algorithm is compared and analyzed with mainstream anomaly detection algorithms on publicly available datasets, showing superior performance and flexibility.
INTERNATIONAL JOURNAL OF APPROXIMATE REASONING
(2024)
Article
Computer Science, Artificial Intelligence
Mario Alviano, Francesco Bartoli, Marco Botta, Roberto Esposito, Laura Giordano, Daniele Theseider Dupre
Summary: This paper investigates the relationships between a multipreferential semantics in defeasible reasoning and a multilayer neural network model. Weighted knowledge bases are considered for a simple description logic with typicality under a concept-wise multipreference semantics. The semantics is used to interpret MultiLayer Perceptrons (MLPs) preferentially. Model checking and entailment based approach are employed in verifying conditional properties of MLPs.
INTERNATIONAL JOURNAL OF APPROXIMATE REASONING
(2024)
Article
Computer Science, Artificial Intelligence
Bazin Alexandre, Galasso Jessie, Kahn Giacomo
Summary: Formal concept analysis is a mathematical framework that represents the information in binary object-attribute datasets using a lattice of formal concepts. It has been extended to handle more complex data types, such as relational data and n-ary relations. This paper presents a framework for polyadic relational concept analysis, which extends relational concept analysis to handle relational datasets consisting of n-ary relations.
INTERNATIONAL JOURNAL OF APPROXIMATE REASONING
(2024)
Article
Computer Science, Artificial Intelligence
Ander Gray, Marcelo Forets, Christian Schilling, Scott Ferson, Luis Benet
Summary: The presented method combines reachability analysis and probability bounds analysis to handle imprecisely known random variables. It can rigorously compute the temporal evolution of p-boxes and provide interval probabilities for formal verification problems. The method does not impose strict constraints on the input probability distribution or p-box and can handle multivariate p-boxes with a consonant approximation method.
INTERNATIONAL JOURNAL OF APPROXIMATE REASONING
(2024)
Article
Computer Science, Artificial Intelligence
Laszlo Csato
Summary: This paper studies a special type of incomplete pairwise comparison matrices and proposes a new method to determine the missing elements without violating the ordinal property.
INTERNATIONAL JOURNAL OF APPROXIMATE REASONING
(2024)
