Article
Computer Science, Artificial Intelligence
Luqing Wang, Junwei Luo, Hongjun Wang, Tianrui Li
Summary: This paper proposes a Markov clustering ensemble (MCE) model to address the weak stability and robustness of soft clustering ensemble. By treating the base clustering algorithms as new features and using the maximum information coefficient to measure their similarity, a graph-based cluster ensemble model is constructed. Experimental results demonstrate that the MCE algorithm outperforms other algorithms in terms of accuracy and purity.
KNOWLEDGE-BASED SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Peng Zhou, Xia Wang, Liang Du
Summary: Unsupervised feature selection is an important task in machine learning but suffers from stability and robustness issues due to the absence of labels. This paper proposes a novel bi-level feature selection ensemble method that not only ensembles at the feature level but also learns a consensus clustering result to guide the feature selection, outperforming other state-of-the-art methods.
INFORMATION FUSION
(2023)
Article
Neurosciences
Laura Masaracchia, Felipe Fredes, Mark W. Woolrich, Diego Vidaurre
Summary: Unsupervised, data-driven methods are commonly used in neuroscience to automatically decompose data into interpretable patterns. However, the impact of assumptions on specific data decompositions is often unclear, limiting the model's applicability and interpretability. This study aims to better characterize the behavior of two types of HMMs applied to electrophysiological data, exploring the significance of different data features and providing guidance for interpretation and analysis.
JOURNAL OF NEUROPHYSIOLOGY
(2023)
Article
Computer Science, Information Systems
Yubo Wang, Shelesh Krishna Saraswat, Iraj Elyasi Komari
Summary: Ensemble clustering, which combines the results of multiple clustering methods, is a challenging research direction in data mining. This study introduces a parallel hierarchical clustering approach using divide-and-conquer strategy to achieve faster and more efficient ensemble clustering. A cluster consensus selection approach is proposed, which selects a subset of primary clusters to participate in the final consensus based on sample-cluster and cluster-cluster similarity. The proposed scheme also incorporates an unsupervised feature selection approach to remove irrelevant features. Extensive evaluations on datasets show that the proposed scheme outperforms state-of-the-art clustering methods, improving average performance by 6% to 24%.
JOURNAL OF KING SAUD UNIVERSITY-COMPUTER AND INFORMATION SCIENCES
(2023)
Article
Computer Science, Artificial Intelligence
Isidoros Perikos, Spyridon Kardakis, Ioannis Hatzilygeroudis
Summary: The article introduces a novel, interpretable HMM-based method for recognizing sentiments in text, which has been tested under various architectures, training methods, orders, and ensembles, showing competitive performance and outperforming traditional HMM methods.
KNOWLEDGE-BASED SYSTEMS
(2021)
Article
Chemistry, Analytical
Ting Lin, Miao Wang, Min Yang, Xu Yang
Summary: This paper addresses the issues with commonly used methods in mining time series data by proposing a novel approach that utilizes Wasserstein distance and autoencoder to learn discrete features and hidden Markov model to learn continuous features. The two models are then stacked to create an ensemble model with lower computational complexity and comparable classification accuracy to state-of-the-art methods.
Article
Computer Science, Theory & Methods
Salvatore D. Tomarchio, Antonio Punzo, Antonello Maruotti
Summary: This paper introduces HMMs for analyzing matrix-variate balanced longitudinal data, assuming a matrix normal distribution for each hidden state. The issue of potential overparameterization is addressed by using the eigen decomposition of covariance matrices, leading to 98 HMMs. An expectation-conditional maximization algorithm is discussed for parameter estimation. The proposed models are validated on simulated and real data sets.
STATISTICS AND COMPUTING
(2022)
Review
Automation & Control Systems
Keyvan Golalipour, Ebrahim Akbari, Seyed Saeed Hamidi, Malrey Lee, Rasul Enayatifar
Summary: Clustering aims to discover natural groupings of patterns, points, or objects without a deterministic approach to decide the best method for a given set of input data. Clustering ensemble combines computed solutions of base clustering algorithms to achieve stability and robustness, while clustering ensemble selection chooses a subset of base clustering based on quality and diversity for better performance. This survey covers the historical development of data clustering, basic clustering techniques, clustering ensemble algorithms, and clustering ensemble selection techniques for improved quality and diversity.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2021)
Article
Computer Science, Information Systems
Teng Li, Amin Rezaeipanah, ElSayed M. Tag El Din
Summary: This paper presents a clustering framework based on ensemble approaches, utilizing Agglomerative Hierarchical Clustering (AHC) methods and a novel similarity measurement. The proposed algorithm, Meta-Clustering Ensemble scheme based on Model Selection (MCEMS), outperforms HMM, DSPA, and WHAC algorithms in terms of clustering accuracy.
JOURNAL OF KING SAUD UNIVERSITY-COMPUTER AND INFORMATION SCIENCES
(2022)
Article
Computer Science, Artificial Intelligence
Chih-Hsuan Wang, Jen-Zen Chen
Summary: This research integrates business analytics with enterprise risk management to help firms implement a decision support war-room. By utilizing a novel framework including hidden Markov models, ensemble learning techniques, and probabilistic Bayes network, managers can improve SMEs' financial performance and predict bankruptcy risk effectively. Key indicators such as cash equivalents, gross sales profit, and operating revenue prove to be crucial in enhancing SMEs' financial health.
Article
Computer Science, Theory & Methods
Yu Luo, David A. Stephens
Summary: This study focuses on modeling data generated by a latent continuous-time Markov jump process, proposing a reversible jump Markov chain Monte Carlo algorithm and applying it to model-based clustering analysis.
STATISTICS AND COMPUTING
(2021)
Article
Computer Science, Information Systems
Mohammad Sultan Mahmud, Joshua Zhexue Huang, Rukhsana Ruby, Alladoumbaye Ngueilbaye, Kaishun Wu
Summary: This paper proposes a distributed computing framework to tackle the challenging task of clustering a big distributed dataset. The approach uses multiple random samples to compute an ensemble result as an estimation of the true result of the dataset. The framework proves to be efficient and scalable in clustering big datasets.
IEEE TRANSACTIONS ON BIG DATA
(2023)
Article
Computer Science, Artificial Intelligence
Carlos Puerto-Santana, Pedro Larranaga, Concha Bielza
Summary: This article introduces asymmetric hidden Markov models with feature saliencies, which are capable of simultaneously determining relevant variables/features and probabilistic relationships between variables during their learning phase. Comparing with other approaches, the proposed models have better or equal fitness and provide further data insights.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Peng Zhou, Xia Wang, Liang Du, Xuejun Li
Summary: Clustering ensemble method integrates multiple base clustering results to improve the stability and robustness of single clustering methods. By dynamically learning a hypergraph with clear clustering structure, our proposed method outperforms conventional hypergraph-based ensemble methods as well as state-of-the-art clustering ensemble methods.
INFORMATION FUSION
(2022)
Article
Spectroscopy
Shaohui Yu, Jing Liu
Summary: This paper proposes an ensemble calibration model FDA-EM-PLS (functional data analysis-ensemble learning-partial least squares) for near-infrared spectroscopy, based on the functional data analysis method. By dividing the near-infrared spectroscopy into intervals and conducting functional data analysis, clustering, and Monte Carlo sampling, this model achieves accurate detection of corn and soil data.
SPECTROCHIMICA ACTA PART A-MOLECULAR AND BIOMOLECULAR SPECTROSCOPY
(2022)
Article
Engineering, Electrical & Electronic
Zhenli He, Fengtao Nan, Xinfa Li, Shin-Jye Lee, Yun Yang
IET INTELLIGENT TRANSPORT SYSTEMS
(2020)
Article
Chemistry, Multidisciplinary
Dehai Zhang, Linan Liu, Qi Wei, Yun Yang, Po Yang, Qing Liu
APPLIED SCIENCES-BASEL
(2020)
Article
Computer Science, Information Systems
Yuelong Xia, Ke Chen, Yun Yang
Summary: In this study, a novel stacked ensemble approach that simultaneously exploits label correlations and the process of learning weights of ensemble members is proposed. Experimental results demonstrate that the proposed algorithm outperforms related state-of-the-art methods in multi-label classification tasks.
INFORMATION SCIENCES
(2021)
Article
Computer Science, Artificial Intelligence
Yun Yang, Jing Guo, Qiongwei Ye, Yuelong Xia, Po Yang, Amin Ullah, Khan Muhammad
Summary: This study proposes a novel transfer learning method combined with ensemble learning for medical decision-making, reducing distribution variances from different perspectives by applying various transfer learning methods and achieving superior performance compared to currently available alternatives.
APPLIED SOFT COMPUTING
(2021)
Article
Computer Science, Information Systems
Cheng Xie, Beibei Yu, Zuoying Zeng, Yun Yang, Qing Liu
Summary: This article proposes a knowledge graph-based multilayer IoT middleware that introduces a new layer to bridge the gap between devices with different communication protocols and can uniformly manage all devices. Evaluation of the proposed approach in a real-world project shows that it effectively resolves communication gap and heterogeneous access issues in the system.
IEEE INTERNET OF THINGS JOURNAL
(2021)
Article
Computer Science, Hardware & Architecture
Fan Cheng, Congtao Wang, Xingyi Zhang, Yun Yang
Summary: The paper introduces an efficient local-expansion-based overlapping-community detection algorithm (OCLN) which reduces computational cost and enhances scalability for community detection in large-scale networks by utilizing local-neighborhood information. Additionally, a belonging coefficient is proposed to filter out incorrectly identified nodes.
IEEE-ACM TRANSACTIONS ON NETWORKING
(2021)
Article
Computer Science, Artificial Intelligence
Cheng Xie, Hongxin Xiang, Ting Zeng, Yun Yang, Beibei Yu, Qing Liu
Summary: This study introduces a generative network-based ZSL approach with Cross Knowledge Learning (CKL) and Taxonomy Regularization (TR) to address the cross modality and cross domain challenges in ZSL, improving the recognition of unseen classes and enhancing the image classification and retrieval performance.
Article
Computer Science, Artificial Intelligence
Yun Yang, Yulong Rao, Minghao Yu, Yan Kang
Summary: The study leveraged AI technology to build herb recommendation models, introducing herb property information and proposing a multi-layer information fusion graph convolution model. The performance of the model outperformed baseline models, aiding in a deeper understanding of TCM prescriptions and exploration of new formulas.
Article
Computer Science, Artificial Intelligence
Yujie Tao, Yun Yang, Po Yang, Fengtao Nan, Yan Zhang, Yulong Rao, Fei Du
Summary: This paper proposes a novel feature relearning method for automatic sleep staging based on single-channel electroencephalography (EEG) to solve the problems of current sleep staging methods. The proposed method utilizes a bottom-up and top-down network structure and the attention mechanism to fully learn EEG information, thereby improving classification performance and achieving automatic sleep classification. Experimental results on the Sleep-EDF dataset demonstrate that the proposed method outperforms state-of-the-art methods.
COMPLEX & INTELLIGENT SYSTEMS
(2023)
Article
Automation & Control Systems
Yun Yang, Yuanyuan Hu, Xingyi Zhang, Song Wang
Summary: The article introduces a novel deep tree training strategy and achieves better performance in medical image classification through a two-stage selective ensemble of CNN branches.
IEEE TRANSACTIONS ON CYBERNETICS
(2022)
Article
Computer Science, Artificial Intelligence
Fei Du, Yun Yang, Ziyuan Zhao, Zeng Zeng
Summary: This paper proposes a novel Efficient Perturbation Inference and Expandable Network (EPIE-Net) to address the issue of forgetting previous data in neural networks. The method dynamically expands task-specific decoders and utilizes a mixed-label uncertainty strategy to improve robustness. Experimental results demonstrate superior performance compared to other methods with fewer parameters in class incremental learning benchmarks.
Article
Computer Science, Information Systems
Shuhao Zhang, Gaoshan Bi, Jun Qi, Yun Yang, Xiangzeng Kong, Fengtao Nan, Menghui Zhou, Po Yang
Summary: To control the first wave of the COVID-19 pandemic in 2020, many models have been effective in predicting the spread of the virus and the impact of interventions. However, few models can collect real-time data quickly while protecting privacy and considering the impact of virus variants and mass vaccination programs. Therefore, we developed a mobile intelligent application that can collect real-time data and conducted a feasibility study using a new COVID-19 mathematical model.
IEEE INTERNET OF THINGS JOURNAL
(2023)
Article
Computer Science, Information Systems
Pei Wang, Yun Yang, Yuelong Xia, Kun Wang, Xingyi Zhang, Song Wang
Summary: This paper proposes an information maximization adaptation network with label distribution priors to address the challenges brought by pseudo labels in unsupervised domain adaptation. By maximizing source mutual information, introducing weighted target mutual information, and adding a regularization term of label priors distribution, this method achieves remarkable results on three benchmark datasets.
IEEE TRANSACTIONS ON MULTIMEDIA
(2023)
Article
Computer Science, Artificial Intelligence
Yujie Tao, Yun Yang, Po Yang, Fengtao Nan, Yan Zhang, Yulong Rao, Fei Du
Summary: This paper proposes a novel feature relearning method for automatic sleep staging based on single-channel electroencephalography (EEG). By using a bottom-up and top-down network and the attention mechanism, the method improves the performance of sleep classification.
COMPLEX & INTELLIGENT SYSTEMS
(2022)
Article
Engineering, Biomedical
Dehai Zhang, Yongchun Duan, Jing Guo, Yaowei Wang, Yun Yang, Zhenhui Li, Kelong Wang, Lin Wu, Minghao Yu
Summary: This study proposes a pathological images analysis method based on multi-instance learning to predict the efficacy of neoadjuvant chemoradiotherapy for rectal cancer. The method includes a gated attention normalization mechanism, a bilinear attention multi-scale feature fusion mechanism, and a weighted loss function. Experimental results demonstrate excellent predictive performance of the method on multiple datasets.
IEEE JOURNAL OF TRANSLATIONAL ENGINEERING IN HEALTH AND MEDICINE
(2022)
Article
Computer Science, Artificial Intelligence
Guang-Hai Liu, Zuo-Yong Li, Jing-Yu Yang, David Zhang
Summary: This article introduces a novel image retrieval method that improves retrieval performance by using sublimated deep features. The method incorporates orientation-selective features and color perceptual features, effectively mimicking these mechanisms to provide a more discriminating representation.
PATTERN RECOGNITION
(2024)
Article
Computer Science, Artificial Intelligence
Fengguang Peng, Zihan Ding, Ziming Chen, Gang Wang, Tianrui Hui, Si Liu, Hang Shi
Summary: RGB-Thermal (RGB-T) semantic segmentation is an emerging task that aims to improve the robustness of segmentation methods under extreme imaging conditions by using thermal infrared modality. The challenges of foreground-background distinguishment and complementary information mining are addressed by proposing a cross modulation process with two collaborative components. Experimental results show that the proposed method achieves state-of-the-art performances on current RGB-T segmentation benchmarks.
PATTERN RECOGNITION
(2024)
Article
Computer Science, Artificial Intelligence
Baihong Han, Xiaoyan Jiang, Zhijun Fang, Hamido Fujita, Yongbin Gao
Summary: This paper proposes a novel automatic prompt generation method called F-SCP, which focuses on generating accurate prompts for low-accuracy classes and similar classes. Experimental results show that our approach outperforms state-of-the-art methods on six multi-domain datasets.
PATTERN RECOGNITION
(2024)
Article
Computer Science, Artificial Intelligence
Huikai Liu, Ao Zhang, Wenqian Zhu, Bin Fu, Bingjian Ding, Shengwu Xiong
Summary: Adverse weather conditions present challenges for computer vision tasks, and image de-weathering is an important component of image restoration. This paper proposes a multi-patch skip-forward structure and a Residual Deformable Convolutional module to improve feature extraction and pixel-wise reconstruction.
PATTERN RECOGNITION
(2024)
Article
Computer Science, Artificial Intelligence
Oliver M. Crook, Mihai Cucuringu, Tim Hurst, Carola-Bibiane Schonlieb, Matthew Thorpe, Konstantinos C. Zygalakis
Summary: The transportation LP distance (TLP) is a generalization of the Wasserstein WP distance that can be applied directly to color or multi-channelled images, as well as multivariate time-series. TLP interprets signals as functions, while WP interprets signals as measures. Although both distances are powerful tools in modeling data with spatial or temporal perturbations, their computational cost can be prohibitively high for moderate pattern recognition tasks. The linear Wasserstein distance offers a method for projecting signals into a Euclidean space, and in this study, we propose linear versions of the TLP distance (LTLP) that show significant improvement over the linear WP distance in signal processing tasks while being several orders of magnitude faster to compute than the TLP distance.
PATTERN RECOGNITION
(2024)
Article
Computer Science, Artificial Intelligence
Haitao Tian, Shiru Qu, Pierre Payeur
Summary: This paper proposes a method of target-dependent classifier, which optimizes the joint hypothesis of domain adaptation into a target-dependent hypothesis that better fits with the target domain clusters through an unsupervised fine-tuning strategy and the concept of meta-learning. Experimental results demonstrate that this method outperforms existing techniques in synthetic-to-real adaptation and cross-city adaptation benchmarks.
PATTERN RECOGNITION
(2024)
Article
Computer Science, Artificial Intelligence
Qingsen Yan, Axi Niu, Chaoqun Wang, Wei Dong, Marcin Wozniak, Yanning Zhang
Summary: Deep learning-based methods have achieved remarkable results in the field of super-resolution. However, the limitation of paired training image sets has led researchers to explore self-supervised learning. However, the assumption of inaccurate downscaling kernel functions often leads to degraded results. To address this issue, this paper introduces KGSR, a kernel-guided network that trains both upscaling and downscaling networks to generate high-quality high-resolution images even without knowing the actual downscaling process.
PATTERN RECOGNITION
(2024)
Article
Computer Science, Artificial Intelligence
Yifan Chen, Xuelong Li
Summary: Gait recognition is a popular technology for identification due to its ability to capture gait features over long distances without cooperation. However, current methods face challenges as they use a single network to extract both temporal and spatial features. To solve this problem, we propose a two-branch network that focuses on spatial and temporal feature extraction separately. By combining these features, we can effectively learn the spatio-temporal information of gait sequences.
PATTERN RECOGNITION
(2024)
Article
Computer Science, Artificial Intelligence
Wei Shi, Wentao Zhang, Wei-shi Zheng, Ruixuan Wang
Summary: This article proposes a simple yet effective visualization framework called PAMI, which does not require detailed model structure and parameters to obtain visualization results. It can be applied to various prediction tasks with different model backbones and input formats.
PATTERN RECOGNITION
(2024)
Article
Computer Science, Artificial Intelligence
Xiaobo Hu, Jianbo Su, Jun Zhang
Summary: This paper reviews the latest technologies in pattern recognition, highlighting their instabilities and failures in practical applications. From a control perspective, the significance of disturbance rejection in pattern recognition is discussed, and the existing problems are summarized. Finally, potential solutions related to the application of compensation on features are discussed to emphasize future research directions.
PATTERN RECOGNITION
(2024)
Article
Computer Science, Artificial Intelligence
Andres Felipe Posada-Moreno, Nikita Surya, Sebastian Trimpe
Summary: Convolutional neural networks are widely used in critical systems, and explainable artificial intelligence has proposed methods for generating high-level explanations. However, these methods lack the ability to determine the location of concepts. To address this, we propose a novel method for automatic concept extraction and localization based on pixel-wise aggregations, and validate it using synthetic datasets.
PATTERN RECOGNITION
(2024)
Article
Computer Science, Artificial Intelligence
Peng Bao, Jianian Li, Rong Yan, Zhongyi Liu
Summary: In this paper, a novel Dynamic Graph Contrastive Learning framework, DyGCL, is proposed to capture the temporal consistency in dynamic graphs and achieve good performance in node representation learning.
PATTERN RECOGNITION
(2024)
Article
Computer Science, Artificial Intelligence
Kristian Schultz, Saptarshi Bej, Waldemar Hahn, Markus Wolfien, Prashant Srivastava, Olaf Wolkenhauer
Summary: Research indicates that deep generative models perform poorly compared to linear interpolation-based methods for synthetic data generation on small, imbalanced tabular datasets. To address this, a new approach called ConvGeN, combining convex space learning with deep generative models, has been proposed. ConvGeN improves imbalanced classification on small datasets while remaining competitive with existing linear interpolation methods.
PATTERN RECOGNITION
(2024)
Article
Computer Science, Artificial Intelligence
Khondaker Tasrif Noor, Antonio Robles-Kelly
Summary: In this paper, the authors propose H-CapsNet, a capsule network designed for hierarchical image classification. The network effectively captures hierarchical relationships using dedicated capsules for each class hierarchy. A modified hinge loss is utilized to enforce consistency among the involved hierarchies. Additionally, a strategy for dynamically adjusting training parameters is presented to achieve better balance between the class hierarchies. Experimental results demonstrate that H-CapsNet outperforms competing hierarchical classification networks.
PATTERN RECOGNITION
(2024)
Article
Computer Science, Artificial Intelligence
Lei Liu, Guorun Li, Yuefeng Du, Xiaoyu Li, Xiuheng Wu, Zhi Qiao, Tianyi Wang
Summary: This study proposes a new agricultural image segmentation model called CS-Net, which uses Simple-Attention Block and Simpleformer to improve accuracy and inference speed, and addresses the issue of performance collapse of Transformers in agricultural image processing.
PATTERN RECOGNITION
(2024)