Article
Computer Science, Artificial Intelligence
Lei Zhao, Ling-Xia Lu, Wei Yao
Summary: This paper introduces a concept of generalized three-way concept lattices for supporting the idea of three-way decisions. The generalized three-way operators are defined and their properties are studied. Two types of generalized three-way concept lattices are constructed based on these operators. The generalized three-way concept lattices provide a more general model compared to Wille's concept lattices and Qi-Wei-Yao's three-way concept lattices.
Article
Computer Science, Artificial Intelligence
Zhen Wang, Chengjun Shi, Ling Wei, Yiyu Yao
Summary: This paper proposes a unified tri-granularity model for attribute reduction in three-way concept lattices, which allows examining the concept lattice at different levels of granularity. It introduces definitions and methods for local granularity and elementary granularity attribute reduction and analyzes their relationship with global granularity. Two attribute reduction algorithms are designed and tested for their effectiveness.
KNOWLEDGE-BASED SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Fei Hao, Jie Gao, Carmen Bisogni, Vincenzo Loia, Zheng Pei, Aziz Nasridinov
Summary: This paper introduces the application of three-way concept analysis (3WCA) in data analysis and discusses the construction and knowledge extraction from three-way concept lattice. By utilizing attribute reduction and three-way concept stability, the size of concept lattice is reduced and informative three-way concepts are extracted to improve the efficiency of knowledge acquisition.
Article
Computer Science, Artificial Intelligence
Zhaohui Qi, Hui Li, Kai Zhang, Jianhua Dai
Summary: In recent years, an increasing number of researchers have focused on how to leverage limited information to make more reasonable decisions, thereby reducing the decision-making risk for decision-makers. Three-way decision, as a method for risky decision-making, offers a new approach to address this challenge and has gained significant attention. This paper proposes a novel three-way utility decision model oriented to attribute fuzzy concept in multi-attribute environments, based on the utility theory.
APPLIED SOFT COMPUTING
(2023)
Article
Computer Science, Information Systems
Yu Fang, Xue-Mei Cao, Xin Wang, Fan Min
Summary: This paper proposes a rapid attribute reduction method based on three-way sampling (3WS-RAR), which improves the effectiveness and efficiency of attribute reduction through three main steps and experiments on large-scale datasets, demonstrating better performance on public benchmark datasets compared to state-of-the-art models.
INFORMATION SCIENCES
(2022)
Article
Computer Science, Artificial Intelligence
Qian Hu, Keyun Qin, Lei Yang
Summary: This paper primarily focuses on a constructing approach to object-induced three-way concept lattices for multi-granularity formal contexts, and the experimental results demonstrate the effectiveness and advantages of the proposed method.
INTERNATIONAL JOURNAL OF APPROXIMATE REASONING
(2022)
Article
Computer Science, Information Systems
Jiajia Wang, Xueling Ma, Jianhua Dai, Jianming Zhan
Summary: This paper presents a novel method to solve multi-attribute decision-making problems under the hesitant fuzzy environment using three-way decision theory. By defining the loss function, establishing the relationship between the loss function and evaluation values, providing an aggregated loss function, and utilizing a conditional probability method, the method effectively addresses actual medical diagnosis problems through comparison with representative methods and experimental evaluations.
INFORMATION SCIENCES
(2021)
Article
Computer Science, Artificial Intelligence
Qian Hu, Keyun Qin, Lei Yang
Summary: This paper studies the updating methods of object-induced three-way concept lattices for dynamic formal contexts. The proposed algorithms for adding attributes or objects and deleting objects or attributes are found to be more effective and advantageous compared to the latest construction algorithms.
APPLIED INTELLIGENCE
(2023)
Article
Computer Science, Information Systems
Wenjie Wang, Jianming Zhan, Jusheng Mi
Summary: The article presents a novel three-way multi-attribute decision-making model based on probabilistic dominance relations and intuitionistic fuzzy sets, and demonstrates its effectiveness and applicability through experimental analysis.
INFORMATION SCIENCES
(2022)
Article
Computer Science, Artificial Intelligence
Zhong Yuan, Hongmei Chen, Peng Xie, Pengfei Zhang, Jia Liu, Tianrui Li
Summary: This paper investigated attribute reduction methods in fuzzy rough set theory, comparing and analyzing three different types of reduction rules through experiments, which can retain fewer attributes while improving or maintaining the classification accuracy of a classifier. Furthermore, some new research directions were discussed.
APPLIED SOFT COMPUTING
(2021)
Article
Computer Science, Information Systems
Chengying Wu, Qinghua Zhang, Yunlong Cheng, Mao Gao, Guoyin Wang
Summary: A novel three-way generative classifier, 3WGC-WSD, is proposed in this study based on the advantages of the Naive Bayes classifier and three-way classifier to improve classification performances through weighted scoring distribution. The model introduces a non-parametric binary generative classifier and utilizes a self-adaptive attribute weighted algorithm to relax the assumption of attribute conditional independence.
INFORMATION SCIENCES
(2021)
Article
Computer Science, Artificial Intelligence
Enliang Yan, Cunguo Yu, Liming Lu, Wenxue Hong, Chunzhi Tang
Summary: This paper discusses the application and experimental results of the incremental concept cognitive learning algorithm based on three-way object partial order structure diagram in dynamic concept learning, demonstrating its compliance with human cognitive principles and performance enhancement.
KNOWLEDGE-BASED SYSTEMS
(2021)
Article
Computer Science, Information Systems
Wenjie Wang, Jianming Zhan, Chao Zhang
Summary: Multi-attribute decision making (MADM) is a crucial part of modern decision sciences, with three-way decisions (3WD) being able to reduce decision risks and improve accuracy compared to traditional two-way decisions (2WD). This paper presents a new 3WD-MADM model based on probabilistic dominance relations, and validates its effectiveness through comparative and experimental analyses.
INFORMATION SCIENCES
(2021)
Article
Computer Science, Information Systems
Can Gao, Jie Zhou, Duoqian Miao, Jiajun Wen, Xiaodong Yue
Summary: The paper introduces a three-way co-decision model for partially labeled data, focusing on semi-supervised attribute reduction algorithms and classification of unlabeled data to improve model performance.
INFORMATION SCIENCES
(2021)
Article
Computer Science, Information Systems
Kehua Yuan, Weihua Xu, Wentao Li, Weiping Ding
Summary: This paper proposes an incremental learning mechanism based on progressive fuzzy three-way concept for object classification in dynamic environments. By defining object and attribute learning operators to obtain fuzzy three-way concept and considering the progressive process of concept learning, a progressive fuzzy three-way concept and its corresponding concept space are learned. The effectiveness of object classification mechanism and dynamic update mechanism based on the progressive concept space is verified through numerical experiments, and an incremental learning mechanism is designed for dynamic increased data.
INFORMATION SCIENCES
(2022)
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)