Article
Computer Science, Artificial Intelligence
Qinghua Zhang, Chengying Wu, Shuyin Xia, Fan Zhao, Man Gao, Yunlong Cheng, Guoyin Wang
Summary: In this study, a novel granular ball computing method is proposed to address the limitations of the existing method. The proposed method introduces a quality index and an incremental mechanism, improving the learning capability and dynamic decision handling. Experimental results demonstrate the effectiveness and efficiency of the proposed method in classification tasks.
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
(2023)
Article
Mathematics, Applied
Wei Li, Bin Yang, Junsheng Qiao
Summary: In this paper, the authors introduce the concepts of overlap and grouping functions and provide a description of (O, G)-granular variable precision fuzzy rough sets. They also propose a new expression of upper and lower approximation operators using fuzzy implications and co-implications. Additionally, (O, G)-GVPFRSs are represented under different fuzzy relations based on construction methods. Finally, some conclusions on GVPFRSs are extended to (O, G)-GVPFRSs under certain conditions.
COMPUTATIONAL & APPLIED MATHEMATICS
(2023)
Article
Computer Science, Information Systems
Zhouming Ma, Jusheng Mi, Yiting Lin, Jinjin Li
Summary: Variable precision rough set (VPRS) has been widely studied as an essential way of knowledge representation and acquisition in uncertainty theory. This paper investigates the corresponding CVPRS model based on a covering-based rough set model, and systematically studies its algebraic structures and properties. An attribute reduction approach is proposed for a covering-based decision information system using the CVPRS model, and the performances of different boundary operators and related indices in these reduction methods are compared. Necessity rules and possibility rules extraction methods corresponding to decision classes are established, and their validity and security are theoretically verified.
INFORMATION SCIENCES
(2022)
Article
Computer Science, Artificial Intelligence
Shuyin Xia, Cheng Wang, Guoyin Wang, Xinbo Gao, Weiping Ding, Jianhang Yu, Yujia Zhai, Zizhong Chen
Summary: This article introduces a granular-ball rough set (GBRS) model based on granular-ball computing, which can process continuous data and use equivalence classes for knowledge representation. Experimental results demonstrate that GBRS outperforms traditional rough set models in terms of learning accuracy and feature selection.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2023)
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
Qiu Jin, Ling-Qiang Li
Summary: This paper introduces 14 types of L-fuzzy variable precision rough sets and provides their axiomatic characterizations, which have not been explored before even in the context of classical rough sets and fuzzy rough sets.
Article
Computer Science, Artificial Intelligence
Jiayue Chen, Ping Zhu
Summary: This paper proposes an extended model called variable precision multigranulation rough sets (VPMGRSs), which introduces rough membership function and approximation parameters from variable precision rough sets (VPRSs) into the multigranulation environment. The relationships between VPMGRSs and other rough sets methods are investigated, along with several VPMGRSs-based attribute reductions. A heuristic algorithm for alpha-lower distribution reduct is also proposed, and its effectiveness and efficiency are demonstrated through a comparative experiment on real datasets.
Article
Computer Science, Artificial Intelligence
Wentao Li, Weihua Xu, Xiaoyan Zhang, Jia Zhang
Summary: This paper discusses the application of a local rough set model based on dominance relation in ordered information systems, as well as the construction of multigranulation rough set models and the updating process of dynamic objects. Experimental evaluation demonstrates the superiority and effectiveness of the proposed dynamic updating approaches in ordered information systems.
ARTIFICIAL INTELLIGENCE REVIEW
(2022)
Article
Computer Science, Theory & Methods
Chun Yong Wang, Lijuan Wan
Summary: The study focuses on granular variable precision fuzzy rough sets based on fuzzy (co)implications to rectify defects, discussing equivalent expressions and composition. Further research looks into rectifying faults using appropriate semicontinuity.
FUZZY SETS AND SYSTEMS
(2021)
Article
Computer Science, Information Systems
Yueli Zhou, Guoping Lin
Summary: This paper introduces the local generalized multigranulation variable precision tolerance rough sets model based on the concept of local multigranulation tolerance rough sets in set-valued decision information systems, and defines the concepts of lower approximate quality, inner and outer importance of attributes.
Article
Computer Science, Information Systems
Bin Yu, Yan Hu, Jianhua Dai
Summary: This study introduces a new model based on variable precision rough sets to enhance the fault tolerance of attribute reduction algorithms, and designs a novel algorithm to delete redundant and noise attributes to improve classification accuracy.
INFORMATION SCIENCES
(2023)
Article
Chemistry, Multidisciplinary
Xiaohong Xiang, Zhiqiang Feng, Hao Yuan, Xianping Zeng, Zufu Pan, Xin Li, Quan Li, Xiaohu Huang
Summary: Considering the complexities of the welding process and the variation between individuals in welding experience, this research introduces rough set theory for modeling and quality control of arc welding. A variable precision neighborhood rough-fuzzy method is proposed to enhance the efficiency and adaptability of rough set theory in welding process control. By designing different welding experiments, descriptors such as the tail area coefficient and the length-width ratio of the melt pool are used to characterize the welding process. The results show that the proposed model has excellent stability and effectiveness.
APPLIED SCIENCES-BASEL
(2023)
Article
Computer Science, Artificial Intelligence
Yan Li, Xiaoxue Wu, Xizhao Wang
Summary: In this paper, a new incremental reduction method based on granular balls and attribute grouping is proposed for dynamic information systems with multiple attribute additions. Different neighborhood radii are adaptively determined when the attribute set changes, and the number of neighborhood granules can also be effectively reduced. Furthermore, the attributes are grouped based on the k-means algorithm, and only attributes from different groups or with small relevance to those in the current reduction set are considered to be incorporated as a reduction attribute, thus reducing the computation time and simultaneously preserving informative attributes.
INTERNATIONAL JOURNAL OF APPROXIMATE REASONING
(2023)
Article
Computer Science, Artificial Intelligence
Ye Du, Bingxue Yao
Summary: This article introduces the covering-based compact and loose variable precision fuzzy rough set models proposed by Zhan and Jiang, as well as their important properties and relationship with the original models. The models are also applied to decision-making problems and validated using a simple example.
JOURNAL OF INTELLIGENT & FUZZY SYSTEMS
(2022)
Article
Computer Science, Information Systems
Xin Yang, Miaomiao Li, Hamido Fujita, Dun Liu, Tianrui Li
Summary: In this paper, a method for improving the performance of attribute reduction in a dynamic data environment is proposed. By combining incremental technology and accelerated reduction strategy, the method utilizes stable attribute groups and matrix-based incremental mechanisms for reduction search and dynamic attribute reduction. Experimental results demonstrate the effectiveness of the proposed method in terms of stability, computational cost, and classification accuracy.
INFORMATION SCIENCES
(2022)
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
Wenwen Liu, Jie Hu, Shengdong Du, Hongmei Chen, Fei Teng
Summary: This research formulates the ABSA task as an opinion triplet extraction (OTE) task under the multi-task learning framework and proposes a method of sharing sentence vectors to enhance the extraction ability of text semantic representation. By using a concurrent way to extract multiple sub-tasks, it reduces error propagation and achieves better results.
NEURAL PROCESSING LETTERS
(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
Medical Laboratory Technology
Wentao Yuan, Ronghua Fang, Chunyan Mao, Hongmei Chen, Bojun Tai, Hui Cong
Summary: This study found that serum hsa_circ_0000702 is variably expressed in gastric cancer patients, suggesting its potential as a novel biomarker for diagnosis and dynamic monitoring of gastric cancer.
JOURNAL OF CLINICAL LABORATORY ANALYSIS
(2023)
Article
Engineering, Electrical & Electronic
Hongmei Chen, Jian Wang, Lanyu Wang, Long LI, Honghui Deng, Xu Meng, Yongsheng Yin
Summary: This paper proposes a fully digital modulation calibration technique for channel mismatch of TIADC at any frequency. It estimates and stores the mismatch errors by pre-inputting a test signal in TIADC, and extracts the stored values for compensation when the input signal is at a special frequency. By adjusting the operation order and the order of correlation and modulation, the complexity of the proposed calibration algorithm is greatly reduced. The hardware consumption of filters in the calibration algorithm is also greatly reduced by introducing a CSD coding technique based on Horner's rule and sub-expression sharing. The FPGA verification results show significant improvements in SFDR and SNDR after calibration.
IEICE TRANSACTIONS ON ELECTRONICS
(2023)
Article
Computer Science, Information Systems
Tengyu Yin, Hongmei Chen, Zhong Yuan, Tianrui Li, Keyu Liu
Summary: Feature selection is important in multilabel learning, and fuzzy rough set theory is widely used in this field. This study focuses on the noise-tolerant fuzzy neighborhood rough set model and its feature selection strategy for multilabel learning. A parameterized hybrid fuzzy similarity relation is introduced to granulate multilabel data, and a noise-resistant feature selection algorithm is proposed.
INFORMATION SCIENCES
(2023)
Article
Computer Science, Artificial Intelligence
Ghufran Ahmad Khan, Jie Hu, Tianrui Li, Bassoma Diallo, Shengdong Du
Summary: Multi-view data are collected from different sources or domains with consistent properties. Existing approaches for multi-view subspace clustering neglect the individual view's knowledge and only consider consistent or specific representations, resulting in poor performance. To address this, we propose a novel strategy that encodes self-representation structure through both consistent and specific representations. Experiments on benchmark datasets demonstrate the effectiveness of our method over state-of-the-art algorithms.
APPLIED INTELLIGENCE
(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
Computer Science, Information Systems
Jia Liu, Wei Huang, Hao Li, Shenggong Ji, Yajun Du, Tianrui Li
Summary: Dangerous driving behaviors are the main cause of most traffic accidents, and detecting these behaviors accurately is a crucial research area in Intelligent Transportation System (ITS). This paper proposes a Symbolic Aggregate approXimation (SAX) and Long Short-Term Memory (LSTM)-based Attention Fusion method (SLAFusion) to improve the detection of dangerous driving behavior.
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
Bassoma Diallo, Jie Hu, Tianrui Li, Ghufran Ahmad Khan, Xinyan Liang, Hongjun Wang
Summary: In the field of deep learning, multi-view learning is an effective method for handling data from different sources. However, the current deep learning approach faces challenges in independently driving neural networks for different perspectives and in calculating network statistics for a large number of viewpoints. This research proposes a novel multi-view deep embedding clustering (MDEC) model that addresses these challenges and demonstrates promising results on small and large-scale multi-view data.
PATTERN RECOGNITION
(2023)
Article
Materials Science, Multidisciplinary
Jun Zhou, Hengcheng Liao, Hongmei Chen, Di Feng, Weijun Zhu
Summary: A coherent dual-phase structure was obtained in Fe3.5Ni3.5Cr2MnAl0.7 high-entropy alloy through vacuum arc-melting and subsequent homogenizing. The microstructure and mechanical properties were examined, revealing a relationship between the face-centered cubic (FCC) matrix phase and the body-centered cubic (BCC) minor phase that meets the Kurdjumov-Sachs orientation. The as-homogenized Fe3.5Ni3.5Cr2MnAl0.7 HEA exhibits excellent mechanical properties due to the synergic effect of the soft FCC phase and hard BCC phase.
Article
Computer Science, Artificial Intelligence
Min Li, Guoyin Wang, Zeng Yu, Hongjun Wang, Jihong Wan, Tianrui Li
Summary: Gaussian mixture model (GMM) is widely used in various domains like data mining. The unsupervised learning of finite mixture model based on minimum message length (MML) criterion allows adaptive model selection and parameter estimation. However, datasets with hierarchical structure pose a challenge as the MML criterion inaccurately considers the hierarchical structure, making it difficult to balance model complexity and goodness of fitting. To address this, a locally consistent GMM with hierarchical MML criterion (GM-HMML) algorithm is proposed, which incorporates a hierarchical MML criterion to control the competition between necessary components and regularizes it using graph Laplacian to avoid overfitting problems. The presented approach enhances component annihilation and achieves good model order and clustering accuracy on real datasets.
INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS
(2023)
Article
Computer Science, Artificial Intelligence
Jia Liu, Tianrui Li, Shenggong Ji, Peng Xie, Shengdong Du, Fei Teng, Junbo Zhang
Summary: Urban flow analysis is an important research for smart city construction, focusing on the continuous state of urban flow. This paper proposes a knowledge mining network for regional flow pattern to mine and store the urban flow pattern. The proposed model consists of two modules, extracting features of the region and its flow pattern as the entity and relation, and using POI features to enhance the embedding representation. Knowledge triplets of regional flow patterns are mined based on the translation distance method. Experimental results show the effectiveness of the proposed model, and visualization of knowledge triplets and application examples are presented.
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
(2023)
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)
