Article
Computer Science, Artificial Intelligence
Binbin Sang, Hongmei Chen, Lei Yang, Tianrui Li, Weihua Xu, Chuan Luo
Summary: Based on the fuzzy dominance neighborhood rough set, this study proposes incremental feature selection approaches for dynamic interval-valued ordered data, and experimentally verifies the robustness of the proposed metric and the effectiveness of the incremental algorithms.
KNOWLEDGE-BASED SYSTEMS
(2021)
Article
Computer Science, Artificial Intelligence
Shuangjie Li, Kaixiang Zhang, Yali Li, Shuqin Wang, Shaoqiang Zhang
Summary: Feature selection is crucial in many fields, especially in machine learning. The proposed method OFS-Gapknn effectively addresses the challenges of online streaming features by defining a new neighborhood rough set relation and analyzing relevance and redundancy features. Experimental results demonstrate the dominance and significance of this method.
APPLIED SOFT COMPUTING
(2021)
Article
Computer Science, Artificial Intelligence
Binbin Sang, Hongmei Chen, Lei Yang, Dapeng Zhou, Tianrui Li, Weihua Xu
Summary: This study extends the dominance-based rough set approach to multi-criteria decision analysis and sorting problems, proposing incremental attribute reduction methods for dynamic ordered data. Experimental results on various datasets demonstrate the effectiveness and efficiency of these algorithms in achieving attribute reduction in dynamic ordered data.
KNOWLEDGE-BASED SYSTEMS
(2021)
Article
Computer Science, Artificial Intelligence
Binbin Sang, Hongmei Chen, Lei Yang, Tianrui Li, Weihua Xu
Summary: This study investigates incremental feature selection approaches for dynamic ordered data, proposing a new conditional entropy with robustness as an evaluation metric for features and designing two incremental feature selection algorithms. Experimental results demonstrate the robustness of the proposed metric and the effectiveness and efficiency of the incremental algorithms in updating reducts for dynamic ordered data.
IEEE TRANSACTIONS ON FUZZY SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Yanzhou Pan, Weihua Xu, Qinwen Ran
Summary: Dominance-based neighborhood rough set provides qualitative and quantitative descriptions of relations between ordered objects but ignores the significance of features. To address this, we propose the weighted dominance-based neighborhood rough set and use conditional entropy and a heuristic algorithm for feature selection.
INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS
(2023)
Article
Computer Science, Artificial Intelligence
Bin Yu, Hengjie Xie, Ruihui Xu, Tian Yang, Zeshui Xu
Summary: By comparing the attributes of objects, a dominance-inferiority relation and a dominance-inferiority-based rough set are introduced. Based on these, the dominance-inferiority degree and dominance-inferiority neighborhood degree are proposed, leading to a novel multiple-attribute decision-making method. The effectiveness, accuracy, and non-randomness of the new method are verified through comparison with other methods using ROC curves.
Article
Computer Science, Artificial Intelligence
Lei Yang, Keyun Qin, Binbin Sang, Chao Fu
Summary: Incremental attribute reduction aims to improve the efficiency of obtaining reduct from the dynamic data. A novel incremental attribute reduction method based on quantitative dominance-based neighborhood self-information (QD-NSI) for dynamic hybrid ordered decision system is proposed. The method considers both deterministic and possible classification information, and the corresponding matrix calculation method and heuristic attribute reduction algorithm are designed. Experimental results show that the proposed algorithm can effectively delete irrelevant or redundant attributes and the efficiency of the incremental algorithms is better than that of the existing related algorithms.
KNOWLEDGE-BASED SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Ahmed Hamed, Hamed Nassar
Summary: IHISs are on the rise, requiring innovative FS solutions. The GWNO algorithm, utilizing GWO and RST, demonstrated impressive test results and superior performance.
Article
Automation & Control Systems
Xiaoyan Zhang, Jianglong Hou, Jirong Li
Summary: This paper introduces a new model called neighborhood dominance rough set (NDRS) by studying the intuitionistic fuzzy neighborhood dominance relation, which can accurately reflect the dominance relations in actual data and select the required data according to different conditions. Experiments on nine UCI datasets validate the feasibility and effectiveness of the proposed model.
INTERNATIONAL JOURNAL OF FUZZY SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Keyu Liu, Tianrui Li, Xibei Yang, Xin Yang, Dun Liu
Summary: This paper introduces a novel ensemble feature selection method, which selects features with local significance through cross-class sample granulation and ensemble feature selection strategies.
APPLIED SOFT COMPUTING
(2022)
Article
Automation & Control Systems
Bin Yu, Ruihui Xu, Zeshui Xu, Jianhua Dai
Summary: This paper improves and optimizes the group-oriented multi-attribute decision-making method based on mixed advantage-disadvantage degree (GOMADMMADD). It designs the advantaged neighborhood operator, disadvantaged neighborhood operator, and advantage-disadvantage neighborhood degree (ADND) to construct the group-oriented multi-attribute decision-making method based on dominance-based rough set (GMADMDRS). Experimental results demonstrate the effectiveness of the optimized GMADMDRS method in handling group-oriented evaluation and ranking problems.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2023)
Article
Mathematics, Applied
Sisi Xia, Lin Chen, Haoran Yang
Summary: This paper introduces the concept of neighborhood soft set and its corresponding decision system to address decision-making problems with heterogeneous information. By defining neighborhood soft set and related operations, as well as decision system and core attribute, decision rules are derived for optimal decision-making. Finally, an algorithm based on the neighborhood soft set is presented and applied in medical diagnosis, with a comparison analysis conducted against other decision-making methods.
Article
Computer Science, Artificial Intelligence
Junyi Chai
Summary: The paper introduces a new type of rough set approximation based on classes rather than class unions and extends it to a series of DRSA models, including classical DRSA, variable consistency DRSA model, variable precision DRSA model, and believable rough set approach model. Additionally, methods of criteria reduction under the class-based rough approximation framework are explored, and relationships among the proposed and previous reducts in DRSA are clarified.
Article
Computer Science, Artificial Intelligence
Jihong Wan, Hongmei Chen, Zhong Yuan, Tianrui Li, Xiaoling Yang, BinBin Sang
Summary: A novel feature selection method considering feature interaction is proposed in this study, with an algorithm called NCMI_IFS developed. Experimental results demonstrate that the algorithm exhibits higher classification performance and significant effectiveness on multiple public datasets.
KNOWLEDGE-BASED SYSTEMS
(2021)
Article
Computer Science, Artificial Intelligence
Jinghua Liu, Yaojin Lin, Weiping Ding, Hongbo Zhang, Cheng Wang, Jixiang Du
Summary: In this paper, a novel multi-label feature selection method based on label distribution and neighborhood rough set (LDRS) is proposed. The method captures the significance of labels and evaluates the quality of features, while considering label-specific features. Experimental results demonstrate the advantages of the proposed method.
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, Artificial Intelligence
Jia Liu, Wei Huang, Tianrui Li, Shenggong Ji, Junbo Zhang
Summary: This paper proposes a multi-domain item-item recommendation method based on cross-domain knowledge graph embedding, which addresses the sparsity and cold start problems faced by traditional recommender systems by analyzing the association between items within the same domain and the interaction between items across diverse domains with the aid of a rich information knowledge graph.
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
(2023)
Article
Computer Science, Artificial Intelligence
Wenlu Yang, Hongjun Wang, Yinghui Zhang, Zehao Liu, Tianrui Li
Summary: Representation learning based on autoencoders has attracted great attention due to its potential to capture valuable latent information. However, traditional autoencoders only focus on minimal reconstruction error and neglect the discrimination of feature representation in machine learning tasks. To overcome this limitation, an enhanced self-supervised discriminative fuzzy autoencoder (FAE) is proposed, which explores information within data to guide unsupervised training and enhance feature discrimination.
ACM TRANSACTIONS ON INTELLIGENT SYSTEMS AND TECHNOLOGY
(2023)
Article
Computer Science, Artificial Intelligence
Lingzhi Wang, Hongmei Chen, Bo Peng, Tianrui Li, Tengyu Yin
Summary: This study proposes a robust MFS method, which addresses the issues in multi-label feature selection using graph regularization and matrix factorization. The effectiveness of the algorithm is demonstrated through experiments.
APPLIED INTELLIGENCE
(2023)
Article
Computer Science, Artificial Intelligence
Pengfei Zhang, Tianrui Li, Zhong Yuan, Zhixuan Deng, Guoqiang Wang, Dexian Wang, Fan Zhang
Summary: This article proposes a novel information system based on possibility distribution, along with several defined measures of information quality. Based on this, an unsupervised feature selection algorithm is designed, which can effectively combine multiple possibilistic information while minimizing information uncertainty.
IEEE TRANSACTIONS ON FUZZY SYSTEMS
(2023)
Review
Computer Science, Artificial Intelligence
Xiaobo Zhang, Donghai Zhai, Tianrui Li, Yuxin Zhou, Yang Lin
Summary: This article systematically summarizes and analyzes the literature on deep learning-based image inpainting. It reviews the research status of deep learning technology in the field of image inpainting over the past 15 years and deeply studies existing image restoration methods based on different neural network structures. The article also provides constructive suggestions for future development and discusses the urgent issues that need to be solved in the field.
INFORMATION FUSION
(2023)
Article
Computer Science, Information Systems
Dexian Wang, Tianrui Li, Ping Deng, Jia Liu, Wei Huang, Fan Zhang
Summary: This paper proposes a generalized deep learning multi-view clustering (GDLMC) algorithm based on non-negative matrix factorization (NMF), which improves the clustering performance of multi-view clustering by addressing the issues of weak feature extraction, slow convergence speed, and low accuracy in NMF based algorithms. The GDLMC algorithm utilizes decoupled and non-negatively restricted matrix elements, updates the elements using stochastic gradient descent with learning rate guidance, and combines generalized weights and biases with activation functions to construct generalized deep learning (GDL), which is then used to learn low-dimensional matrices for each view and a consensus matrix. Experimental results on four public datasets demonstrate the significant advantages of GDLMC.
IEEE TRANSACTIONS ON BIG DATA
(2023)
Article
Computer Science, Theory & Methods
Chuan Luo, Sizhao Wang, Tianrui Li, Hongmei Chen, Jiancheng Lv, Zhang Yi
Summary: This article introduces a Rough Hypercuboid based Distributed Online Feature Selection (RHDOFS) method to address the challenges of Volume and Velocity in Big Data. It proposes a novel integrated feature evaluation criterion by exploring class separability in the boundary region. An efficient online feature selection method is developed for streaming features, and a parallel optimization mechanism is employed to accelerate the implementation. The algorithm is implemented on Apache Spark and demonstrates superior performance in comparison to other online feature selection algorithms.
IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Jielei Chu, Jing Liu, Hongjun Wang, Hua Meng, Zhiguo Gong, Tianrui Li
Summary: Based on the idea of small perturbation, a representation learning model based on probability distribution is proposed, and two variant models, Micro-DGRBM and Micro-DRBM, are introduced. The KL divergence of SPI is minimized within the same cluster to promote the similarity of probability distributions, while it is maximized across different clusters to enforce the dissimilarity in CD learning. Experimental results demonstrate that the proposed deep Micro-DL architecture outperforms the baseline method and other shallow models and deep frameworks for clustering.
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
(2023)
Article
Medicine, Research & Experimental
Wenyun Huang, Wensi Niu, Hongmei Chen, Wujun Jiang, Yanbing Fu, Xiuxiu Li, Minglei Li, Jun Hua, Chunxia Hu
Summary: We aimed to develop a nomogram to predict the risk of severe influenza in previously healthy children. A total of 1135 children infected with influenza in a retrospective cohort study were included. Risk factors were identified through logistic regression analysis and a nomogram was established. The nomogram showed good predictive ability in both the training and validation cohorts.
JOURNAL OF INTERNATIONAL MEDICAL RESEARCH
(2023)
Article
Computer Science, Artificial Intelligence
Lei Ma, Chuan Luo, Tianrui Li, Hongmei Chen, Dun Liu
Summary: With the accumulation of interesting data in various application fields, incremental datasets are becoming more common. However, selecting informative attributes from dynamically changing datasets poses challenges. Therefore, an incremental processing mechanism is desired to update the attribute reducts efficiently. In this paper, a novel dynamic graph-based fuzzy rough attribute reduction approach is proposed to handle the maintenance of fuzzy rough attribute reduction in dynamic data, which outperforms existing methods in terms of speed and quality preservation.
INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS
(2023)
Article
Computer Science, Artificial Intelligence
Weiyi Li, Hongmei Chen, Tianrui Li, Tengyu Yin, Chuan Luo
Summary: In this paper, a robust unsupervised feature selection method, DSLRAS, is proposed, which can capture the correlation between features and the correlation between samples through latent representation learning in both feature space and data space. Adaptive graph learning is used to maintain the local geometric structure of data more accurately, and a regularization term is added to guarantee row-sparsity and achieve better results.
INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS
(2023)
Article
Computer Science, Artificial Intelligence
Bo Xiong, Hongmei Chen, Tianrui Li, Xiaoling Yang
Summary: Multi-view graph clustering has attracted extensive research attention due to its ability to capture consistent and complementary information between views. However, multi-view data are mostly high-dimensional and may contain redundant and irrelevant features. In addition, the original data are often contaminated by noise and outliers, affecting the reliability of the learned affinity matrix. This study proposes a robust multi-view clustering model that combines low-dimensional and low-rank latent space learning, self-representation learning, and multi-view discrepancy induction fusion. Experimental results on benchmark datasets show that the proposed model outperforms state-of-the-art comparison models in terms of robustness and clustering performance.
APPLIED INTELLIGENCE
(2023)
Article
Infectious Diseases
Hongmei Chen, Mingze Tang, Lemeng Yao, Di Zhang, Yubin Zhang, Yingren Zhao, Han Xia, Tianyan Chen, Jie Zheng
Summary: mNGS is a novel nucleic acid method that can detect unknown and difficult pathogenic microorganisms. Its application in the etiological diagnosis of fever of unknown origin (FUO) is not well studied. This study aimed to comprehensively assess the value of mNGS in diagnosing FUO and investigate its impact on diagnosis time, hospitalization days, antibiotic consumption, and cost.
BMC INFECTIOUS DISEASES
(2023)
Article
Computer Science, Artificial Intelligence
Zhihong Wang, Hongmei Chen, Zhong Yuan, Jihong Wan, Tianrui Li
Summary: This paper introduces a feature selection method based on multiscale fuzzy entropy, which improves the effectiveness of feature selection by fusing granule information at different scales.
IEEE TRANSACTIONS ON FUZZY 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)
