Article
Computer Science, Artificial Intelligence
Xianyong Zhang, Yanhong Zhou, Xiao Tang, Yunrui Fan
Summary: This study aims to improve the conditional neighborhood entropy by establishing three-level granular structures and three-way neighborhood entropies. The improved measurement method provides more accurate, hierarchical, systematic, and monotonic measurements. The effectiveness of the method is verified through decision table examples and data set experiments, facilitating uncertainty measurement, information processing, and knowledge discovery.
INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS
(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, 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
Hongyuan Gou, Xianyong Zhang, Jilin Yang, Zhiying Lv
Summary: This paper systematically constructs three-way fusion measures by combining algebraic and informational measures, and hierarchically investigates three-level feature selections. The new algorithms outperform existing ones in classification performance according to data experiments.
APPLIED SOFT COMPUTING
(2023)
Article
Chemistry, Multidisciplinary
Xinhui Zhang, Tinghui Ouyang
Summary: This paper proposes an advanced method for forming three-way decision classification rules, which uses information granules and information entropy to describe uncertainty and form fuzzy rules to solve classification problems. Experimental results show that classification rules considering uncertain data perform better in decision-making processes and have an improvement compared to traditional methods.
APPLIED SCIENCES-BASEL
(2022)
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)
Review
Computer Science, Artificial Intelligence
Xin Yang, Yanhua Li, Tianrui Li
Summary: The concept of three-way decision, focusing on thinking, problem solving, and information processing in threes, has been extensively studied and applied in the fields of machine learning and data engineering in recent years. The integration of dynamic and uncertainty through multi-granularity learning in an open-world environment has brought new vitality to three-way decision. This paper investigates and summarizes the initial and development models of three-way decision, traces the historical line of sequential three-way decision from rough set to granular computing, and proposes a unified framework of three-way multi-granularity learning with four crucial problems on mining uncertain regions continuously. Additionally, proposals on three-way decision associated with open-continual learning are provided.
INTERNATIONAL JOURNAL OF APPROXIMATE REASONING
(2023)
Article
Computer Science, Artificial Intelligence
Xin Yang, Yang Chen, Hamido Fujita, Dun Liu, Tianrui Li
Summary: In this paper, a novel framework of sequential three-way decision for the fusion of mixed data from the subjective and objective dynamic perspectives is explored. The proposed models achieve lower decision cost and acceptable accuracy, as demonstrated by comparative experiments.
KNOWLEDGE-BASED SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Wenbin Qian, Yangyang Zhou, Jin Qian, Yinglong Wang
Summary: This paper proposes a cost-sensitive sequential three-way decision model for information systems with fuzzy decision, which achieves better classification performance and lower test costs by optimizing information granularity.
INTERNATIONAL JOURNAL OF APPROXIMATE REASONING
(2022)
Article
Computer Science, Information Systems
Jin Ye, Jianming Zhan, Weiping Ding, Hamido Fujita
Summary: This paper introduces a new 3WD model applied to realistic MCDM problems and discusses related issues in a fuzzy probabilistic rough set model through a decision information system. A novel approach is established for classifying and ranking applicants for enterprise talent recruitment problems, with the feasibility of the method confirmed. Experimental results demonstrate the better performance in terms of effectiveness and stability of the constructed MCDM approach.
INFORMATION SCIENCES
(2022)
Article
Computer Science, Artificial Intelligence
Wenbin Qian, Yanqiang Tu, Jin Qian, Wenhao Shu
Summary: In this paper, a novel disambiguation-free PML approach named PMLTT is proposed, which uses mutual cooperation and iteration between classifiers to correct noisy labels and improve the performance of the learning model. The three-way decision and precise supervisory information are also utilized to solve conflicts and make predictions more accurate. Experimental results show that the proposed approach effectively reduces the negative influence of noisy labels and learns a robust model.
KNOWLEDGE-BASED SYSTEMS
(2023)
Article
Computer Science, Information Systems
Xiangbin Liu, Wang Mao, Jianhua Dai, Kai Zhang
Summary: This paper proposes a new three-way decision model based on comprehensive fuzzy concepts and applies it to solve multi-criteria decision-making problems. Comparative analysis shows that the method is feasible and stable.
INFORMATION SCIENCES
(2022)
Article
Computer Science, Artificial Intelligence
Pei Liang, Wanying Cao, Junhua Hu
Summary: This paper investigates the application of the sequential three-way decision (S3WD) model in classification and proposes sequential three-way classifiers (S3WCs) to address risk preference and decision conflict. Experimental results demonstrate the superior classification performance of the proposed models on diverse datasets.
APPLIED SOFT COMPUTING
(2023)
Article
Computer Science, Artificial Intelligence
Chuan Luo, Qian Cao, Tianrui Li, Hongmei Chen, Sizhao Wang
Summary: Attribute reduction is an important data preprocessing technique in data mining, and this paper proposes a parallel attribute reduction algorithm based on neighborhood rough sets using Apache Spark. The algorithm achieves parallel computation in a distributed computing environment, and experimental results show its superior parallel performance and scalability.
EXPERT SYSTEMS WITH APPLICATIONS
(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)
Review
Computer Science, Interdisciplinary Applications
Wanjun Xia, Tianrui Li, Chongshou Li
Summary: In this paper, we propose a new framework for systematically surveying scientific impact prediction research. We consider the four common academic entities: papers, scholars, venues, and institutions. We review all reported prediction tasks and categorize input features into six groups. Furthermore, we classify forecasting methods into different categories and subdivisions based on their characteristics. Finally, we discuss open issues, existing challenges, and potential research directions.
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
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
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, Artificial Intelligence
Keyu Liu, Tianrui Li, Xibei Yang, Hengrong Ju, Xin Yang, Dun Liu
Summary: Neighborhood granulation is a fundamental strategy for feature evaluation and selection, but it neglects observations across different levels of granularity. To address this issue, a novel algorithm called N3Y is proposed, which incorporates neighborhood relevancy, redundancy, and granularity interactivity. N3Y outperforms other feature selectors in extensive experiments.
APPLIED SOFT COMPUTING
(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
Toshitaka Hayashi, Dalibor Cimr, Hamido Fujita, Richard Cimler
Summary: This paper proposes a novel preprocessing technique called image entropy equalization to eliminate the differences in image entropy. It compares the original and equalized images in various machine learning tasks, and shows that image entropy equalization can improve the AUC score for one-class autoencoder, as well as achieve fair results for classification and regression tasks.
INFORMATION SCIENCES
(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, Information Systems
Jie Hu, Zhanao Hu, Tianrui Li, Shengdong Du
Summary: Time series forecasting has wide applications in our daily lives, and traditional supervised models have limitations due to a lack of real-time annotated data. Self-supervised methods, particularly contrastive learning, are proposed as a solution to this problem, but the direct transfer of data augmentation techniques from computer vision is not suitable for the time domain. In this paper, we introduce a novel time series forecasting model called ACST, which utilizes disentangled seasonal-trend representation and an improved generative adversarial data augmentation method for contrastive loss. Experimental results show that ACST achieves an average improvement of 26.8% on six benchmarks.
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
Chuan Luo, Sizhao Wang, Tianrui Li, Hongmei Chen, Jiancheng Lv, Zhang Yi
Summary: This paper presents parallel feature selection algorithms based on the rough hypercuboid approach to handle growing data volumes. Experimental results show that our algorithms are significantly faster than the original sequential counterpart while guaranteeing result quality. Moreover, the proposed algorithms can effectively utilize distributed-memory clusters to handle computationally demanding tasks.
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
(2023)
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, 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)
