Article
Computer Science, Information Systems
Hong Wang, Jingtao Guan
Summary: This study investigates the dynamic knowledge updating of optimistic and pessimistic multigranulation decision-theoretic rough sets using the matrix method. Static and dynamic algorithms were proposed and the time complexity of the three algorithms was analyzed, showing that the dynamic algorithms are more effective.
Article
Computer Science, Artificial Intelligence
Hong Wang, Jingtao Guan
Summary: In recent years, the amount of data we need to handle has been increasing, which also applies to multi-granulation rough sets. Therefore, updated schemes have been proposed to deal with the variation of attributes or attribute values in multi-granulation rough sets. This paper presents a dynamic mechanism to update the approximations in multi-granulation rough sets when increasing or decreasing objects. The relationships between the original approximations and updated approximations are explored, and dynamic algorithms with their time complexity are provided.
Article
Computer Science, Information Systems
Zhan-Ao Xue, Haodong Hou, Bingxin Sun, Yongxiang Li, Yanna Zhang
Summary: The study focuses on the application of the multi-granulation fuzzy probabilistic rough set model over two universes and proposes incremental algorithms based on the object-first approach for updating approximations efficiently.
Article
Computer Science, Artificial Intelligence
Yi Xu, Quan Wang, Weikang Sun
Summary: This paper proposes a matrix-based incremental updating approach for approximations in multigranulation rough set model under two-dimensional variations.
INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS
(2021)
Article
Computer Science, Artificial Intelligence
Haoxiang Zhou, Wentao Li, Chao Zhang, Tao Zhan
Summary: In the new era of information society, studying incremental methods of calculating approximations and constructing incremental algorithms is wise to save computing time in rough methods. This study focuses on maintaining approximations dynamically in interval-valued ordered decision systems when the feature set and sample set increase or decrease. Incremental updating rules for four circumstances are obtained based on the matrix expression of approximations and dominated sets, and incremental algorithms are derived accordingly. Comparative experiments on calculation time verify the effectiveness and superiority of the proposed dynamic algorithms.
APPLIED INTELLIGENCE
(2023)
Article
Computer Science, Artificial Intelligence
Deyou Xia, Guoyin Wang, Qinghua Zhang, Jie Yang, Shuai Li, Man Gao
Summary: This study proposes a feature selection method based on rough sets and incremental learning, which shows higher efficiency in dynamic information systems. By introducing the nonincremental approximation feature selection method and the incremental theory of fuzzy knowledge distance, and developing an acceleration mechanism to eliminate redundant information granules, the study demonstrates the effectiveness and efficiency of the proposed method.
IEEE TRANSACTIONS ON FUZZY SYSTEMS
(2023)
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
Xin Yang, Yujie Li, Dun Liu, Tianrui Li
Summary: The approximation learning of fuzzy concepts associated with fuzzy rough sets and three-way decisions is an important technology for handling uncertain knowledge. This article explores the connection and interplay between fuzzy rough approximations and three-way approximations, proposing a hierarchical fuzzy rough approximation method in a dynamic fuzzy open-world environment. The article also discusses the interpretation and representation of fuzzy three-way regions in fuzzy rough sets. Experimental results demonstrate the effectiveness of the proposed hierarchical approximation learning models.
IEEE TRANSACTIONS ON FUZZY SYSTEMS
(2022)
Article
Computer Science, Theory & Methods
Shuang An, Mengru Zhang, Changzhong Wang, Weiping Ding
Summary: This paper introduces the successful application of fuzzy rough set theory in uncertainty measurement. A novel uncertainty measure based on kNN granules is proposed, and a robust fuzzy rough set model is constructed. A feature selection algorithm and a semi-supervised feature selection algorithm are designed based on the model, and their feasibility, effectiveness, and robustness are validated through experiments.
FUZZY SETS AND SYSTEMS
(2023)
Article
Computer Science, Information Systems
Lirun Su, Fusheng Yu, Jinjin Li, Xubo Du, Hanliang Huang
Summary: In real applications, the feature set in a relation decision system often varies with time, making existing attribute reduction methods time-consuming and unsuitable. This study proposes an incremental updating mechanism for the positive region and right neighbor of a relation decision system to efficiently update the reduction with prior information. By integrating this mechanism with a positive region-based reduction method, a novel incremental updating reduction algorithm for relation decision systems with dynamic conditional relation sets was designed. Experimental results on UCI datasets demonstrate its good performance.
INFORMATION SCIENCES
(2023)
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
Mathematics
Musavarah Sarwar
Summary: Rough sets are a crucial tool for modeling uncertainty and vagueness without predefined functions and additional suppositions. This research applies the concept of rough sets to hypergraphs and introduces the novel concept of rough hypergraphs based on rough relations. Various concepts and relations are illustrated and the relationships among certain types of rough hypergraphs are studied in detail.
JOURNAL OF MATHEMATICS
(2022)
Article
Automation & Control Systems
Faryal Nosheen, Usman Qamar, Muhammad Summair Raza
Summary: This paper presents a parallel technique for calculating DRSA approximation sets. The proposed method directly computes approximations by following heuristic rules without calculating dominance positive or negative relations. The proposed parallel approach shows significant improvements in terms of execution time, memory consumption, and algorithmic complexity compared to traditional methods.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2022)
Article
Computer Science, Artificial Intelligence
Weihua Xu, Kehua Yuan, Wentao Li
Summary: The approximation space in rough set theory is crucial for handling uncertainties. The local rough set model, as an effective approach, improves learning efficiency by avoiding unnecessary information granule calculations. This paper investigates the dynamic approximation update mechanism for multigranulation data and proposes corresponding dynamic update algorithms based on the local generalized multigranulation rough set model.
APPLIED INTELLIGENCE
(2022)
Article
Computer Science, Theory & Methods
Lianjie Dong, Ruihong Wang, Degang Chen
Summary: In this study, an incremental mechanism for feature selection in fuzzy rough sets is proposed, which can simultaneously add samples and features. By analyzing the changes in the relative discernibility relationship, a unified method for simultaneously increasing the samples and features is formed. Experimental results show that the algorithm can effectively handle the incremental feature selection task for dynamic data sets.
FUZZY SETS AND SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Chenbing Li, Jie Hu, Tianrui Li, Shengdong Du, Fei Teng
Summary: This paper proposes a novel multi-task learning model for extracting emotion, cause, and emotion-cause pairs from documents. The model utilizes a shared module and a task-specific module to share information between tasks, and employs a sampling-based method to address the problem of label imbalance.
APPLIED INTELLIGENCE
(2023)
Article
Computer Science, Theory & Methods
Zhihong Wang, Hongmei Chen, Zhong Yuan, Tianrui Li
Summary: Dimensionality reduction is an important preprocessing method for data analysis. The proposed hybrid dimensionality reduction method combines fuzzy rough set and linear discriminant analysis to improve interpretability and retain original feature information.
FUZZY SETS AND SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Hao Chen, Hongmei Chen, Weiyi Li, Tianrui Li
Summary: This study proposes a semi-supervised feature selection algorithm that combines latent representation learning and sparse graph discriminative model to improve the performance of a learning model. The method can consider both the structure information in data space and feature space, and effectively utilize label information. The feasibility and effectiveness of the proposed method are validated through experiments.
APPLIED INTELLIGENCE
(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
Taha M. Rajeh, Tianrui Li, Chongshou Li, Muhammad Hafeez Javed, Zhpeng Luo, Fares Alhaek
Summary: Urban traffic flow prediction has attracted much attention, especially with the availability of large amounts of traffic data. However, existing traffic flow techniques heavily rely on limited auxiliary information. This paper proposes a framework based on data characteristics and temporal correlation to achieve accurate predictions without additional influential factors.
KNOWLEDGE-BASED 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, Information Systems
Yuqin Yao, Hua Meng, Yang Gao, Zhiguo Long, Tianrui Li
Summary: Dimensionality reduction is a significant data preprocessing technique. This article analyzes the drawbacks of the traditional linear unsupervised dimensionality reduction method LPP and proposes an improved model to resolve them. Furthermore, the article enhances LPP to maintain topological connectivity of data. Experimental results demonstrate that the new model outperforms the original LPP model and other classic linear or non-linear dimensionality reduction methods.
INFORMATION SCIENCES
(2023)
Article
Computer Science, Information Systems
Rasha Al-Huthaifi, Tianrui Li, Wei Huang, Jin Gu, Chongshou Li
Summary: Smart cities have emerged globally in the past decade, utilizing big data and the internet of things to enhance monitoring and integration of infrastructure systems, resulting in more efficient, livable, and sustainable cities. However, the increased vulnerability of big data to attacks and the implementation of stricter regulations for protecting user data present significant privacy and security challenges. Federated learning has gained attention as a method to address these challenges by distributing learning over decentralized data, providing privacy protection and artificial intelligence advantages. This study reviews the application of federated learning in smart city systems and discusses its benefits, drawbacks, research issues, and future directions, emphasizing the need for comprehensive testing to improve data protection and performance in smart cities.
INFORMATION SCIENCES
(2023)
Article
Computer Science, Information Systems
Dongxue Xia, Yan Yang, Shuhong Yang, Tianrui Li
Summary: This paper proposes a Kernelized Graph-based Incomplete Multi-view Clustering (KGIMC) algorithm to overcome the limitation of incomplete data in real-world multi-view clustering. The algorithm optimizes similarity learning, clustering analysis, and kernel completion in a mutual reinforcement manner to achieve an overall optimal clustering result. Experimental results show that KGIMC outperforms the state-of-the-art approaches on popular datasets.
INFORMATION SCIENCES
(2023)
Article
Computer Science, Information Systems
Dexian Wang, Tianrui Li, Ping Deng, Fan Zhang, Wei Huang, Pengfei Zhang, Jia Liu
Summary: In this article, a Generalized Deep Learning Clustering (GDLC) algorithm based on Non-Negative Matrix Factorization (NMF) is proposed to address the slow convergence and low clustering accuracy in the update process of NMF. A nonlinear constrained NMF (NNMF) algorithm is constructed to achieve sequential updates of the matrix elements guided by the learning rate, and the GDLC algorithm is constructed by transforming gradient values into generalized weights and biases through a nonlinear activation function. The experimental results on eight datasets demonstrate the efficient performance of the GDLC algorithm.
ACM TRANSACTIONS ON KNOWLEDGE DISCOVERY FROM DATA
(2023)
Article
Computer Science, Cybernetics
Ping Deng, Tianrui Li, Hongjun Wang, Dexian Wang, Shi-Jinn Horng, Rui Liu
Summary: This article proposes a novel graph regularized sparse NMF algorithm (GSNMF) for data reconstruction tasks, addressing the issue of sensitivity to noise in the square loss method. In addition, an extension algorithm called GSNMTF is also introduced, and experimental results demonstrate its good performance.
IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Keyu Liu, Tianrui Li, Xibei Yang, Hongmei Chen, Jie Wang, Zhixuan Deng
Summary: In this article, a novel semi-supervised feature selection scheme called SemiFREE is proposed, which redefines the feature relevance and redundancy by considering the fuzziness or uncertainty in data labeling. Experimental results demonstrate the superiority of SemiFREE in the presence of partially labeled data.
IEEE TRANSACTIONS ON FUZZY SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Peng Xie, Minbo Ma, Tianrui Li, Shenggong Ji, Shengdong Du, Zeng Yu, Junbo Zhang
Summary: This paper presents a spatio-temporal dynamic graph relational learning model for predicting urban metro station flow. The model captures the traffic patterns of different stations using a node embedding representation module, learns dynamic spatial relationships between metro stations through a dynamic graph relationship learning module, and utilizes a transformer for long-term relationship prediction. Experimental results demonstrate the advantages of our method in urban metro flow prediction.
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
(2023)
Article
Computer Science, Artificial Intelligence
Yanyong Huang, Zongxin Shen, Yuxin Cai, Xiuwen Yi, Dongjie Wang, Fengmao Lv, Tianrui Li
Summary: This paper proposes a complementary and consensus learning-based incomplete multi-view unsupervised feature selection method ((CIMUFS)-I-2) to address the issue of incomplete multi-view data. The method integrates feature selection into an extended weighted non-negative matrix factorization model and achieves complete similarity graph reconstruction for each view.
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
(2023)
Article
Computer Science, Artificial Intelligence
Zonglei Chen, Minbo Ma, Tianrui Li, Hongjun Wang, Chongshou Li
Summary: The development of deep learning technology has greatly improved time series forecasting, particularly in the area of long sequence time series forecasting (LSTF). This article provides a comprehensive survey of LSTF studies with deep learning technology, including rigorous definitions, taxonomy based on network structure, key problems and solutions, evaluation metrics, applications, datasets, and open-source codes. It also presents a comparison of the proposed Kruskal-Wallis test based evaluation method with existing metrics and proposes potential research directions. All resources and codes are available online at https://github.com/Masterleia/TSF_LSTF_Compare.
INFORMATION FUSION
(2023)
Article
Computer Science, Artificial Intelligence
Hao Yang, Min Wang, Zhengfei Yu, Hang Zhang, Jinshen Jiang, Yun Zhou
Summary: In this paper, a novel method called CSTTA is proposed for test time adaptation (TTA), which utilizes confidence-based optimization and sample reweighting to better utilize sample information. Extensive experiments demonstrate the effectiveness of the proposed method.
KNOWLEDGE-BASED SYSTEMS
(2024)
Article
Computer Science, Artificial Intelligence
Jin Liu, Ju-Sheng Mi, Dong-Yun Niu
Summary: This article focuses on a novel method for generating a canonical basis for decision implications based on object-induced operators (OE operators). The logic of decision implication based on OE operators is described, and a method for obtaining the canonical basis for decision implications is given. The completeness, nonredundancy, and optimality of the canonical basis are proven. Additionally, a method for generating true premises based on OE operators is proposed.
KNOWLEDGE-BASED SYSTEMS
(2024)
Review
Computer Science, Artificial Intelligence
Kun Bu, Yuanchao Liu, Xiaolong Ju
Summary: This paper discusses the importance of sentiment analysis and pre-trained models in natural language processing, and explores the application of prompt learning. The research shows that prompt learning is more suitable for sentiment analysis tasks and can achieve good performance.
KNOWLEDGE-BASED SYSTEMS
(2024)
Article
Computer Science, Artificial Intelligence
Xiangjun Cai, Dagang Li
Summary: This paper presents a new decomposition mechanism based on learned decomposition mapping. By using a neural network to learn the relationship between original time series and decomposed results, the repetitive computation overhead during rolling decomposition is relieved. Additionally, extended mapping and partial decomposition methods are proposed to alleviate boundary effects on prediction performance. Comparative studies demonstrate that the proposed method outperforms existing RDEMs in terms of operation speed and prediction accuracy.
KNOWLEDGE-BASED SYSTEMS
(2024)
Article
Computer Science, Artificial Intelligence
Xu Wu, Yang Liu, Jie Tian, Yuanpeng Li
Summary: This paper proposes a blockchain-based privacy-preserving trust management architecture, which adopts federated learning to train task-specific trust models and utilizes differential privacy to protect device privacy. In addition, a game theory-based incentive mechanism and a parallel consensus protocol are proposed to improve the accuracy of trust computing and the efficiency of consensus.
KNOWLEDGE-BASED SYSTEMS
(2024)
Article
Computer Science, Artificial Intelligence
Zaiyang Yu, Prayag Tiwari, Luyang Hou, Lusi Li, Weijun Li, Limin Jiang, Xin Ning
Summary: This study introduces a 3D view-based approach that effectively handles occlusions and leverages the geometric information of 3D objects. The proposed method achieves state-of-the-art results on occluded ReID tasks and exhibits competitive performance on holistic ReID tasks.
KNOWLEDGE-BASED SYSTEMS
(2024)
Article
Computer Science, Artificial Intelligence
Yongliang Shi, Runyi Yang, Zirui Wu, Pengfei Li, Caiyun Liu, Hao Zhao, Guyue Zhou
Summary: Neural implicit representations have gained attention due to their expressive, continuous, and compact properties. However, there is still a lack of research on city-scale continual implicit dense mapping based on sparse LiDAR input. In this study, a city-scale continual neural mapping system with a panoptic representation is developed, incorporating environment-level and instance-level modeling. A tailored three-layer sampling strategy and category-specific prior are proposed to address the challenges of representing geometric information in city-scale space and achieving high fidelity mapping of instances under incomplete observation.
KNOWLEDGE-BASED SYSTEMS
(2024)
Article
Computer Science, Artificial Intelligence
Ruihan Hu, Zhi-Ri Tang, Rui Yang, Zhongjie Wang
Summary: Mesh data is crucial for 3D computer vision applications worldwide, but traditional deep learning frameworks have struggled with handling meshes. This paper proposes MDSSN, a simple mesh computation framework that models triangle meshes and represents their shape using face-based and edge-based Riemannian graphs. The framework incorporates end-to-end operators inspired by traditional deep learning frameworks, and includes dedicated modules for addressing challenges in mesh classification and segmentation tasks. Experimental results demonstrate that MDSSN outperforms other state-of-the-art approaches.
KNOWLEDGE-BASED SYSTEMS
(2024)
Article
Computer Science, Artificial Intelligence
Buliao Huang, Yunhui Zhu, Muhammad Usman, Huanhuan Chen
Summary: This paper proposes a novel semi-supervised conditional normalizing flow (SSCFlow) algorithm that combines unsupervised imputation and supervised classification. By estimating the conditional distribution of incomplete instances, SSCFlow facilitates imputation and classification simultaneously, addressing the issue of separated tasks ignoring data distribution and label information in traditional methods.
KNOWLEDGE-BASED SYSTEMS
(2024)
Article
Computer Science, Artificial Intelligence
Deeksha Varshney, Asif Ekbal, Erik Cambria
Summary: This paper focuses on the neural-based interactive dialogue system that aims to engage and retain humans in long-lasting conversations. It proposes a new neural generative model that combines step-wise co-attention, self-attention-based transformer network, and an emotion classifier to control emotion and knowledge transfer during response generation. The results from quantitative, qualitative, and human evaluation show that the proposed models can generate natural and coherent sentences, capturing essential facts with significant improvement over emotional content.
KNOWLEDGE-BASED SYSTEMS
(2024)
Article
Computer Science, Artificial Intelligence
Junchen Ye, Weimiao Li, Zhixin Zhang, Tongyu Zhu, Leilei Sun, Bowen Du
Summary: Modeling multivariate time series has long been a topic of interest for scholars in various fields. This paper introduces MvTS, an open library based on Pytorch, which provides a unified framework for implementing and evaluating these models. Extensive experiments on public datasets demonstrate the effectiveness and universality of the models reproduced by MvTS.
KNOWLEDGE-BASED SYSTEMS
(2024)
Article
Computer Science, Artificial Intelligence
Reham R. Mostafa, Ahmed M. Khedr, Zaher Al Aghbari, Imad Afyouni, Ibrahim Kamel, Naveed Ahmed
Summary: Feature selection is crucial in classification procedures, but it faces challenges in high-dimensional datasets. To overcome these challenges, this study proposes an Adaptive Hybrid-Mutated Differential Evolution method that incorporates the mechanics of the Spider Wasp Optimization algorithm and the concept of Enhanced Solution Quality. Experimental results demonstrate the effectiveness of the method in terms of accuracy and convergence speed, and it outperforms contemporary cutting-edge algorithms.
KNOWLEDGE-BASED SYSTEMS
(2024)
Article
Computer Science, Artificial Intelligence
Ti Xiang, Pin Lv, Liguo Sun, Yipu Yang, Jiuwu Hao
Summary: This paper introduces a Track Classification Model (TCM) based on marine radar, which can effectively recognize and classify shipping tracks. By using a feature extraction network with multi-feature fusion and a dataset production method to address missing labels, the classification accuracy is improved, resulting in successful engineering application in real scenarios.
KNOWLEDGE-BASED SYSTEMS
(2024)
Article
Computer Science, Artificial Intelligence
Zhihao Zhang, Yuan Zuo, Chenghua Lin, Junjie Wu
Summary: This paper proposes a novel unsupervised context-aware quality phrase mining framework called LMPhrase, which is built upon large pre-trained language models. The framework mines quality phrases as silver labels using a parameter-free probing technique on the pre-trained language model BERT, and formalizes the phrase tagging task as a sequence generation problem by fine-tuning on the Sequence to-Sequence pre-trained language model BART. The results of extensive experiments show that LMPhrase consistently outperforms existing competitors in two different granularity phrase mining tasks.
KNOWLEDGE-BASED SYSTEMS
(2024)
Article
Computer Science, Artificial Intelligence
Kemal Buyukkaya, M. Ozan Karsavuran, Cevdet Aykanat
Summary: The study aims to investigate the hybrid parallelization of the Stochastic Gradient Descent (SGD) algorithm for solving the matrix completion problem on a high-performance computing platform. A hybrid parallel decentralized SGD framework with asynchronous inter-process communication and a novel flexible partitioning scheme is proposed to achieve scalability up to hundreds of processors. Experimental results on real-world benchmark datasets show that the proposed algorithm achieves 6x higher throughput on sparse datasets compared to the state-of-the-art, while achieving comparable throughput on relatively dense datasets.
KNOWLEDGE-BASED SYSTEMS
(2024)
