Article
Physics, Multidisciplinary
Naihua Ji, Rongyi Bao, Xiaoyi Mu, Zhao Chen, Xin Yang, Shumei Wang
Summary: This study highlights the drawbacks of current quantum classifiers in big data environments and proposes a global decision tree paradigm to address these issues. By integrating the Bayesian algorithm and the quantum decision tree classification algorithm, the proposed method achieves high accuracy and efficiency while considering classification costs.
FRONTIERS IN PHYSICS
(2023)
Article
Computer Science, Information Systems
Xiaoqing Ye, Dun Liu
Summary: This paper proposes a novel sequential recommendation strategy from the temporal-spatial perspective, which constructs multilevel recommendation information using recurrent neural network and achieves multi-step recommendation through a temporal-spatial three-way recommendation strategy. A temporal-spatial three-way recommendation based on recurrent neural network is further proposed to realize recommendation with lower decision cost.
INFORMATION SCIENCES
(2022)
Article
Computer Science, Artificial Intelligence
Lidong Wang, Xueqin Liu, Yanjun Wang
Summary: This study focuses on achieving consensus in group decision-making problems in complex and uncertain environments through minimum adjustment and minimum cost. A two-stage consensus optimization model is designed, and Pythagorean fuzzy linguistic preference information is used to achieve a balance between expert opinions.
APPLIED SOFT COMPUTING
(2022)
Article
Computer Science, Artificial Intelligence
Jindong Qin, Luis Martinez, Witold Pedrycz, Xiaoyu Ma, Yingying Liang
Summary: The management of uncertainty in decision-making problems remains a challenging and timely research issue. Granular Computing, a paradigm for handling higher types of uncertainty in decision analysis, is considered a new asset in decision-making studies. This paper provides a comprehensive overview of Granular Computing for decision-making through literature analysis, highlighting its extensions, applications, and challenges.
INFORMATION FUSION
(2023)
Article
Computer Science, Artificial Intelligence
Wenbin Qian, Yangyang Zhou, Jin Qian, Yinglong Wang
Summary: This paper proposes a cost-sensitive sequential three-way decision model for information systems with fuzzy decision, which achieves better classification performance and lower test costs by optimizing information granularity.
INTERNATIONAL JOURNAL OF APPROXIMATE REASONING
(2022)
Article
Computer Science, Information Systems
Lijuan Meng, Bin Bai, Wenda Zhang, Lu Liu, Chunying Zhang
Summary: The paper proposes a decision tree classification algorithm based on granular matrices, which improves the classification accuracy and performance by defining bit-multiplication and bit-sum operations of granular matrices and the similarity between granules.
Article
Computer Science, Artificial Intelligence
Shuyin Xia, Xiaochuan Dai, Guoyin Wang, Xinbo Gao, Elisabeth Giem
Summary: This article introduces a learning method called Granular-ball computing (GBC), which is efficient, robust, and scalable in the field of granular computing. The authors propose a method for accelerating the generation of granular balls (GBs) using division, and achieve a fully adaptive GB generation process by eliminating GB overlap and considering other factors. Experimental results show that these methods have similar accuracies to existing methods while improving in terms of speed and adaptiveness.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Serafin Moral-Garcia, Joaquin Abellan, Tahani Coolen-Maturi, Frank P. A. Coolen
Summary: The study introduces a new cost-sensitive Decision Tree model for Imprecise Classification, which considers error costs by weighting instances. Unlike traditional models, this method utilizes the Nonparametric Predictive Inference Model to provide more informative predictions.
APPLIED SOFT COMPUTING
(2022)
Article
Computer Science, Artificial Intelligence
Xiaofeng Zhu, Jianye Yang, Chengyuan Zhang, Shichao Zhang
Summary: The study introduces a novel Data-driven Incremental Imputation Model (DIM) to effectively and economically impute missing values using all available information in the dataset. By considering both economical criteria and effective imputation information, DIM outperforms comparison methods in terms of prediction accuracy and classification accuracy on UCI datasets.
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
(2021)
Article
Computer Science, Artificial Intelligence
Xingyu Fu, Yingyue Chen, Jingru Yan, Yumin Chen, Feng Xu
Summary: The random forest, a widely used ensemble learning method, has universal applicability but struggles with uncertain data and thus produces poor classification results. To address this issue, a broad granular random forest algorithm is proposed by studying granular computing theory and breadth concepts. The algorithm granulates the relationships between features, defines operation rules for granular vectors, and introduces the granular decision tree model. The final result is obtained through a multiple granular decision tree voting method, yielding better classification performance compared to the traditional random forest algorithm.
JOURNAL OF INTELLIGENT & FUZZY SYSTEMS
(2023)
Article
Management
Yunlong Mi, Zongrun Wang, Hui Liu, Yi Qu, Gaofeng Yu, Yong Shi
Summary: Dynamic classification decision making is an important topic in management decision making and data mining. The existing strategies for static classification decision making are not suitable for mining evolving dynamic data. Additionally, the factors related to incorrect classification predictions are often neglected in standard learning systems. To address these issues, this article proposes a novel framework called granular concept-cognitive computing system (gC3S) that transforms instances into concepts for dynamic classification decision making. Experimental results on traffic data stream mining show the effectiveness of the approach.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2023)
Article
Medicine, General & Internal
Hailing Liu, Jingjing Guo, Lei Cao, Huayuan Zhu, Yi Miao, Xinyi Du, Yujie Wu, Wei Xu, Jianyong Li, Lei Fan
Summary: This study aimed to identify clinical factors associated with survival in patients with T-LGLL and develop a predictive model for guiding therapeutic decision-making. The study found that an Eastern Cooperative Oncology Group performance status over two and a platelet count below 100 x 109/L were independently associated with worse overall survival. The study provided a simplified decision tree model for guiding treatment decisions.
ANNALS OF MEDICINE
(2023)
Article
Computer Science, Artificial Intelligence
Shlomi Maliah, Guy Shani
Summary: Cost sensitive classification aims to minimize the expected cost by deciding on the next attribute to measure after each observation. This paper suggests using POMDPs for cost sensitive classification and identifies potentially important belief states through standard decision trees.
ARTIFICIAL INTELLIGENCE
(2021)
Article
Computer Science, Information Systems
Demetris Trihinas, George Pallis, Marios D. Dikaiakos
Summary: The article introduces a lightweight adaptive monitoring framework suitable for smart IoT devices, which dynamically adjusts monitoring intensity and data dissemination to reduce energy consumption and data volume while maintaining a certain level of accuracy.
IEEE TRANSACTIONS ON SERVICES COMPUTING
(2021)
Article
Computer Science, Artificial Intelligence
Chaojie An, Qifeng Zhou, Shen Yang
Summary: Most existing feature selection methods overlook the costs of acquiring each feature, but in reality, we need to balance model performance and feature costs. To address this, we propose a reinforcement learning agent to guide the cost-sensitive feature acquisition process and a deep learning-based model to select informative and lower-cost features adaptively.
APPLIED SOFT COMPUTING
(2022)
Article
Computer Science, Artificial Intelligence
Xianchao Zhu, Tianyi Huang, Ruiyuan Zhang, William Zhu
Summary: As an important branch of reinforcement learning, Apprenticeship learning focuses on how an agent learns behavioral decisions by observing an expert policy. State abstraction is often used to compress the state space to improve learning efficiency, but finding the right balance is crucial. DIBS aims to address this issue by using the information rate for compression and KL divergence for decision performance evaluation. WDIBS improves upon DIBS by using Wasserstein distance to measure the difference between distributions, ensuring good decision performance even with overlapping support sets.
APPLIED INTELLIGENCE
(2022)
Article
Computer Science, Artificial Intelligence
Ruijia Li, Zhiling Cai, Tianyi Huang, William Zhu
Summary: This paper introduces a new concept called anchor to replace subgoals in hierarchical reinforcement learning, which encourages agents to move fast away from the anchor towards the desired goal. By using intrinsic rewards based on distance from the achieved goal to the anchor and weighted by extrinsic rewards, the proposed method demonstrates effectiveness in continuous control tasks with long horizons.
KNOWLEDGE-BASED SYSTEMS
(2021)
Article
Computer Science, Artificial Intelligence
Jie Hao, Zhiling Cai, Ruijia Li, William Zhu
Summary: Neural architecture search (NAS) has been successful in automatically designing high-performance neural networks, with the proposal of architecture saliency addressing the significant deviation in architecture selection from gradient-based methods.
NEURAL COMPUTING & APPLICATIONS
(2022)
Article
Computer Science, Artificial Intelligence
Min Li, Tianyi Huang, William Zhu
Summary: This research proposes an adaptive exploration policy to address the exploration-exploitation tradeoff by adjusting the exploration noise based on training stability. The effectiveness of this policy is demonstrated through theoretical analysis and experiments.
INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS
(2021)
Article
Computer Science, Artificial Intelligence
Xianchao Zhu, Ruiyuan Zhang, William Zhu
Summary: The paper proposes a new method for option discovery to accelerate exploration in sparse-reward domains by reducing the expected cover time of the environment. The method selects two nonadjacent vertices of the state transition matrix as options, which significantly reduces the exploration time.
KNOWLEDGE-BASED SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Tianyi Huang, Min Li, Xiaolong Qin, William Zhu
Summary: In this paper, a new convolutional deterministic policy is proposed for effective learning in continuous action control by utilizing convolutional neural networks to extract information from state sequences. Experimental results show that this policy enables the agent to take better actions and learn faster than existing reinforcement learning methods for continuous action control.
Article
Computer Science, Artificial Intelligence
Jie Hao, William Zhu
Summary: Community detection is the process of partitioning community nodes into groups with similar attributes and topologies. Deep graph clustering, using graph neural networks, has become a mainstream approach due to its powerful abilities of feature representation and relationship extraction. However, existing methods lack attribute information, leading to a decrease in detection performance. To address this, an enhanced feature representation approach is proposed by incorporating a basic autoencoder and a self-supervised mechanism into the deep graph clustering model. Experimental results demonstrate the improved performance of community detection compared to other methods.
APPLIED INTELLIGENCE
(2023)
Article
Computer Science, Information Systems
Shiping Wang, Shunxin Xiao, William Zhu, Yingya Guo
Summary: Multi-view clustering is a method to improve learning performance by utilizing discriminative information from heterogeneous data sources. This study proposes a novel multi-view fuzzy clustering method that combines deep random walk and sparse low-rank embedding to fully exploit the complementarity and consistency of multi-view data.
INFORMATION SCIENCES
(2022)
Article
Computer Science, Artificial Intelligence
Jie Hao, William Zhu
Summary: The Differentiable architecture search (DARTS) method has made significant progress in designing automated neural architectures. We propose an effective approach called Layered Feature Representation for Differentiable Architecture Search (LFR-DARTS) that can extract layered features from shallow to deep layers, resulting in a competitive performance on various datasets.
Article
Computer Science, Cybernetics
Shiping Wang, Lele Fu, Zhewen Wang, Haiping Xu, William Zhu
Summary: Recent research has proposed a multigraph random walk scheme for addressing multiview clustering and semisupervised classification tasks, which integrates random walk with multiview learning. The scheme recursively learns a globally stable probability distribution matrix from multiple views to obtain label indicators for clustering or semisupervised classification. An adaptive weight vector is also learned to incorporate the diversity and complementarity of multiview data. Comprehensive comparative experiments demonstrate the superiority of the proposed method in terms of both clustering and classification performance.
IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Tianyi Huang, Min Li, William Zhu
Summary: This paper proposes a method that combines the ACP approach with reinforcement learning-based recommender systems to address noise and improve recommendations. By training a parallel environment and using its predicted states, noise in the recommendation process can be reduced. Theoretical analysis and experiments demonstrate that this method outperforms existing recommender systems.
INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS
(2022)
Article
Computer Science, Artificial Intelligence
Min Li, Tianyi Huang, William Zhu
Summary: In this paper, a clustering experience replay method called CER is proposed to effectively exploit the experience hidden in all explored transitions in reinforcement learning. The method divides the training process into several periods, clusters the transitions explored in each period, and constructs a conditional probability density function to ensure sufficient replay of all kinds of transitions.
PATTERN RECOGNITION
(2022)
Article
Computer Science, Cybernetics
Zexi Chen, Yunhe Zhang, Sheng Huang, Yanfang Liu, William Zhu, Shiping Wang
Summary: This article proposes a multiview deep matrix factorization model to learn a shared compact representation from multiview data. By combining multiple matrix factorizations from different views with a shared hidden layer, the model obtains a high-level semantic representation. The nonnegativity constraint of the learned representation is transformed using a projection operation, and the model is trained with a joint loss of reconstruction error and compactness loss. Experiments demonstrate the superiority of the proposed method in multiview clustering.
IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS
(2023)
Article
Computer Science, Cybernetics
Shiping Wang, Jinbin Yang, Jie Yao, Yang Bai, William Zhu
Summary: Graph data and graph node clustering are important in data analysis. Recently, deep neural networks have been used for graph node clustering because of their powerful representation capabilities. This article reviews the latest methods in this field and evaluates their performance on real-world graph datasets using several metrics. The results highlight potential research challenges and directions for deep graph node clustering.
IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS
(2023)
Article
Mathematics, Applied
Jie Hao, William Zhu
Summary: This paper introduces an architecture self-attention mechanism for neural architecture search, which improves the performance stability and efficiency of architecture search by constructing interrelationships among architectures.
JOURNAL OF NONLINEAR AND VARIATIONAL ANALYSIS
(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)