Article
Computer Science, Artificial Intelligence
Yang Liu, Limin Wang, Musa Mammadov, Shenglei Chen, Gaojie Wang, Sikai Qi, Minghui Sun
Summary: Researchers proposed a novel framework called Hierarchical Independence Thresholding (HIT) for efficient identification of informational conditional independence and probabilistic conditional independence, which improves the fit of learned topology to the data. Experimental results demonstrate that applying HIT to BNCs can achieve competitive classification performance.
KNOWLEDGE-BASED SYSTEMS
(2021)
Article
Chemistry, Multidisciplinary
Guiliang Ou, Yulin He, Philippe Fournier-Viger, Joshua Zhexue Huang
Summary: This paper proposes an improved approach for constructing NBC called MAF-NBC, which overcomes the limitations of the NBC through a mixed-attribute fusion mechanism and an improved autoencoder neural network. Experimental results demonstrate that MAF-NBC outperforms eight state-of-the-art Bayesian algorithms in classification performance.
APPLIED SCIENCES-BASEL
(2022)
Article
Computer Science, Artificial Intelligence
Limin Wang, Junyang Wei, Kuo Li, Jiaping Zhou
Summary: Bayesian network classifiers provide a formal framework for probabilistic knowledge representation and reasoning with uncertainty. The addition of strong conditional dependencies can relax independence assumptions, while weak ones may result in biased estimates and degradation in generalization performance. This paper proposes an extension to the k-dependence Bayesian classifier that achieves a bias/variance trade-off by verifying the rationality of implicit independence assumptions. The learned robust topologies accurately fit labeled and unlabeled data through informational and probabilistic dependency relationships.
INTELLIGENT DATA ANALYSIS
(2023)
Article
Computer Science, Artificial Intelligence
He Kong, Limin Wang
Summary: Recent studies have shown the effectiveness of Bayesian network classifiers (BNCs) in knowledge representation and classification. Among them, averaged one-dependence estimators (AODE) stand out due to their ability to balance bias and variance through independence assumptions and ensemble learning. However, unverified independence assumptions can lead to biased estimates and reduced classification performance.
PATTERN RECOGNITION
(2023)
Article
Computer Science, Artificial Intelligence
Zupeng Liang, Shengfen Ji, Qiude Li, Sigui Hu, Yang Yu
Summary: This paper proposes an attribute-weighted isometric embedding (AWIE) method for categorical encoding on mixed data. By using isometric embedding and attribute weighting, AWIE effectively tackles the issue of strong attribute association in the data, and extensive experimental results on 16 datasets show that AWIE significantly improves classification performance compared to 28 competitors.
APPLIED INTELLIGENCE
(2023)
Article
Computer Science, Artificial Intelligence
Limin Wang, Yibin Xie, Meng Pang, Junyang Wei
Summary: This research focuses on improving the performance of Bayesian network classifiers by using a double weighting scheme in AODE. Experimental evaluations show that attribute weighting and model weighting are complementary, and DWAODE demonstrates significant advantages in terms of zero-one loss, bias-variance decomposition, RMSE, Friedman and Nemenyi tests.
KNOWLEDGE-BASED SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Juan C. Alfaro, Juan A. Aledo, Jose A. Gamez
Summary: This paper tackles the partial label ranking problem by transforming the ranking with ties into a set of discrete variables and using Bayesian network classifiers to compute posterior probabilities. The experimental evaluation shows that this method is competitive in accuracy and faster than existing mixture-based probabilistic graphical models.
INTERNATIONAL JOURNAL OF APPROXIMATE REASONING
(2023)
Article
Mathematics
Tingyu Lai, Zhongzhan Zhang
Summary: This article presents a new approach for measuring and testing the conditional mean dependence of a response variable on a predictor variable, combining with the martingale difference divergence (MDD) metric. The proposed method shows higher power for general conditional mean dependence relationships, even in high-dimensional settings, as demonstrated by simulations and real data analysis.
COMMUNICATIONS IN MATHEMATICS AND STATISTICS
(2023)
Article
Psychology, Multidisciplinary
Alaina Talboy, Sandra Schneider
Summary: This study examines the influence of reference dependence on diagnostic reasoning, finding that it leads to a value selection bias where incorrect responses are often consistent with the provided values in the problem. Additionally, individuals are more likely to utilize the superordinate value as part of their solution rather than the anticipated reference class values. The introduction of a new sample weakens the congruence effect and increases reliance on the overall sample size. Overall, higher numerical skills are associated with higher accuracy.
FRONTIERS IN PSYCHOLOGY
(2022)
Article
Computer Science, Artificial Intelligence
He Kong, Xiaohu Shi, Limin Wang, Yang Liu, Musa Mammadov, Gaojie Wang
Summary: The paper proposes a novel approach, averaged tree-augmented one-dependence estimators (ATODE), which relaxes the independence assumption of AODE by exploring higher-order conditional dependencies between attributes. Experimental results on 36 datasets demonstrate that the proposed approach can achieve competitive or better classification performance compared to state-of-the-art learners.
APPLIED INTELLIGENCE
(2021)
Article
Computer Science, Artificial Intelligence
Li-Min Wang, Peng Chen, Musa Mammadov, Yang Liu, Si-Yuan Wu
Summary: A novel weighted AODE algorithm (AWODE) is proposed in this study, which adaptively selects weights to alleviate the independence assumption and make the learned probability distribution fit the instance. Experimental results demonstrate that this approach achieves bias-variance trade-off on 40 benchmark datasets.
INTELLIGENT DATA ANALYSIS
(2021)
Article
Computer Science, Artificial Intelligence
Meng Pang, Limin Wang, Qilong Li, Guo Lu, Kuo Li
Summary: This paper proposes learning k-dependence Bayesian multinet classifiers in the framework of multistage classification to address the issue of asymmetric independence assertion. The experimental results show that the proposed algorithm achieves competitive classification performance compared to other classifiers.
INTELLIGENT DATA ANALYSIS
(2023)
Article
Pharmacology & Pharmacy
Jiangtao Gou
Summary: There are multiple comparison procedures used in confirmatory clinical studies and exploratory research for multiplicity adjustment, including Hochberg and Benjamini-Hochberg procedures. It is a common misconception that these procedures can properly control the type I error rate if the test statistics are independent or positively correlated. In fact, a much stronger positive dependence assumption needs to be satisfied. We provide a comprehensive review of the dependence conditions used in multiple testing procedures, showing that a weaker positive dependence assumption may result in an inflation of type I error rate by a factor of 2 and discuss the type I error rate control under certain negative dependence conditions.
JOURNAL OF BIOPHARMACEUTICAL STATISTICS
(2023)
Article
Mathematical & Computational Biology
Trung Dung Tran, Emmanuel Lesaffre, Geert Verbeke, Joke Duyck
Summary: The study introduces a Bayesian latent vector autoregressive model for analyzing multivariate longitudinal data, focusing on the evolution of latent variables and considering the correlation structure of responses. By addressing local dependence through item-specific random effects, the model corrects biased estimates and measures the magnitude of local dependence. Application of the model to real data demonstrates its effectiveness in providing accurate parameter estimates.
Article
Mathematics, Applied
Jacek Jachymski
Summary: In this paper, we treat the recent result of Beldzinski et al. on the continuous dependence of fixed points as a special case of the contraction principle for a Nemytskii operator on a space of continuous functions. We generalize their result by considering an equicontinuous family of mappings with contractive p-th iterates for some p ∈ N. Furthermore, we obtain a theorem on the continuous dependence of solutions of Cauchy's problem on both the initial values and the parameters. Finally, we establish two remetrization theorems and discuss the possibility of deriving our main result from the Banach contraction principle by using a remetrization of the space.
MATHEMATICAL METHODS IN THE APPLIED SCIENCES
(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)