Article
Computer Science, Information Systems
Guoqiang Wang, Tianrui Li, Pengfei Zhang, Qianqian Huang, Hongmei Chen
Summary: Local rough set models are effective for handling large data sets with small amounts of labeled data, improving computational performance significantly. The double-local rough set framework introduces the concept of local equivalence classes and defines lower deletion matrix, upper addition matrix, and upper deletion matrix. Proposed algorithms in double-local rough sets outperform original counterparts in attribute reduction and knowledge discovery.
INFORMATION SCIENCES
(2021)
Article
Computer Science, Artificial Intelligence
Meng Hu, Eric C. C. Tsang, Yanting Guo, Degang Chen, Weihua Xu
Summary: This study introduces a novel attribute reduction method based on weighted neighborhood relations, which fully mines the correlation between attributes and decisions, assigning higher weights to attributes with higher correlation, achieving good performance results.
KNOWLEDGE-BASED SYSTEMS
(2021)
Article
Computer Science, Information Systems
Zhong Yuan, Hongmei Chen, Tianrui Li, Zeng Yu, Binbin Sang, Chuan Luo
Summary: The study proposed a generalized unsupervised mixed attribute reduction model based on fuzzy rough sets and designed a specific algorithm FRUAR. Experimental results showed that the FRUAR algorithm can select fewer attributes to maintain or improve the performance of learning algorithms.
INFORMATION SCIENCES
(2021)
Article
Computer Science, Information Systems
Can Gao, Jie Zhou, Duoqian Miao, Xiaodong Yue, Jun Wan
Summary: This paper introduces a semi-supervised attribute reduction method based on rough sets, which utilizes information granularity and entropy measure to optimize attribute reduction for partially labeled data by generating proxy labels and using a fast algorithm.
INFORMATION SCIENCES
(2021)
Article
Computer Science, Theory & Methods
Linlin Xie, Guoping Lin, Jinjin Li, Yidong Lin
Summary: Attribute reduction, an essential challenge in pattern recognition, data mining, and knowledge discovery, can be improved by using a new measure called local information entropy.
FUZZY SETS AND SYSTEMS
(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
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, 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
Ziming Luo, Can Gao, Jie Zhou
Summary: This study proposes a rough sets-based tri-trade model for partially labeled data. A new discernibility matrix is first proposed to consider both labeled and unlabeled data, and a beam search-based algorithm is provided to generate multiple semi-supervised reducts. Then, a tri-trade model is developed using three diverse semi-supervised reducts, with a data editing technique embedded to generate reliable pseudo-labels for unlabeled data. Theoretical analysis and comparative experiments on UCI datasets demonstrate that the proposed model effectively utilize unlabeled data to improve generalization performance and outperform other representative methods.
APPLIED INTELLIGENCE
(2023)
Article
Computer Science, Artificial Intelligence
Meng Hu, Yanting Guo, Degang Chen, Eric C. C. Tsang, Qingshuo Zhang
Summary: The construction of fuzzy relations is crucial in fuzzy rough sets, and relations generated by soft distances are more robust. Two enhanced fuzzy similarity relations are proposed to improve attribute reduction and classification, using neighborhood and decision information. The algorithm is validated using gene expression profiles and demonstrates strong noise resistance and selection of tumor-related genes.
KNOWLEDGE-BASED SYSTEMS
(2023)
Article
Computer Science, Information Systems
Xia Ji, JianHua Peng, Peng Zhao, Sheng Yao
Summary: Attribute reduction is a crucial step in data analysis, and the attribute reduction method based on neighborhood rough sets (NRS) is commonly used. However, this method has high time complexity due to its reliance on grid search for radius selection. Granular ball neighborhood rough sets (GBNRS) offer more generality and flexibility than NRS, but they may delete granular balls at class boundaries, leading to the loss of class boundary information. To address these issues, a fuzzy granular ball is defined, and an extended rough sets model (FGBERS) is proposed based on this ball. Experimental results show that FGBERS outperforms GBNRS in terms of classification accuracy, especially on high-dimensional datasets.
INFORMATION SCIENCES
(2023)
Article
Computer Science, Artificial Intelligence
Yizhu Li, Mingjie Cai, Jie Zhou, Qingguo Li
Summary: The notion of multi-granularity has been applied in mathematical models in granular computing. This paper proposes an accelerated algorithm for multi-granularity reduction, addressing the challenges of high computational complexity and difficulty in synthesizing multi-granularity information. The method constructs a multi-granularity reduction structure and integrates multiple granularity information in attribute evaluations, achieving high-quality reduction results with improved time efficiency.
APPLIED INTELLIGENCE
(2022)
Article
Computer Science, Information Systems
Meng Hu, Eric C. C. Tsang, Yanting Guo, Degang Chen, Weihua Xu
Summary: This paper proposes an attribute optimization algorithm based on overlap degree to accelerate attribute reduction and improve classification performance. By modeling the overlap degree of objects from different categories in advance, it can efficiently filter and optimize attributes for decision approximation.
INFORMATION SCIENCES
(2022)
Article
Computer Science, Artificial Intelligence
Jingjing Xie, Bao Qing Hu, Haibo Jiang
Summary: This paper investigates the important application of attribute reduction and proposes a rough set model that considers the weight information of attributes. The experimental results demonstrate the effectiveness of the proposed attribute reduction algorithm on multiple public datasets.
INTERNATIONAL JOURNAL OF APPROXIMATE REASONING
(2022)
Article
Computer Science, Information Systems
Qinli Zhang, Yiying Chen, Gangqiang Zhang, Zhaowen Li, Lijun Chen, Ching-Feng Wen
Summary: The paper discusses the handling of categorical data in machine learning, introducing fuzzy information structures and new uncertainty measurements for considering the equality of attribute values. Numerical experiments and statistical tests were conducted to evaluate the performance of the proposed measurements, showing that they outperform traditional measurements based on I-structures. Furthermore, attribute reduction algorithms based on the new measurements were presented and tested in clustering analysis, showing effective performance in reducing attributes.
INFORMATION SCIENCES
(2021)
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)