Article
Mathematics
Linghao Zhang, Bo Pang, Haitao Tang, Hongjun Wang, Chongshou Li, Zhipeng Luo
Summary: The pcDMDS model enhances discriminative features by incorporating fuzzy k-means for compactness and extended pairwise constraint information between samples. Experimental results show that pcDMDS outperforms the PMDS model in accuracy and purity on twelve datasets.
Article
Computer Science, Artificial Intelligence
Xinyi Chen, Li Zhang, Lei Zhao
Summary: To address the issue of redundant features and high dimensionality in downstream tasks, a semi-supervised feature selection method called HM-ICS is proposed. It utilizes a modified constraint score method to measure feature relevance and maintain data structure. Experimental results show that HM-ICS outperforms state-of-the-art supervised and semi-supervised methods.
KNOWLEDGE-BASED SYSTEMS
(2023)
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
Automation & Control Systems
Biao Wang, Wenjing Wang, Guanglei Meng, Tiankuo Meng, Bin Song, Yingnan Wang, Yuming Guo, Zhihua Qiao, Zhizhong Mao
Summary: This paper proposes an ensemble detector called Selective Feature Bagging (SFB) that simultaneously considers variance and bias reduction. By dynamically selecting the most competent base detectors, SFB improves accuracy without deterioration of diversity. Experimental results demonstrate the superiority of SFB over Feature Bagging (FB) on multiple datasets.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2023)
Article
Computer Science, Information Systems
Qiqi Zhang, Zhongying Zhao, Hui Zhou, Xiangju Li, Chao Li
Summary: Self-supervised learning on heterogeneous graphs has gained attention for its label-free nature. Existing research mainly focuses on predefined meta-paths and struggles with graph noise. To address these issues, we propose HeMuc, a self-supervised contrastive learning method that combines structure and feature constraints. By leveraging graph reachability, we obtain the sequence of target nodes traversed by source nodes in a high-order relation view. In the feature view, we reconstruct the graph structure and eliminate dependence on the original graph. Experimental results demonstrate that HeMuc outperforms state-of-the-art methods in node classification and clustering tasks. Source code is available at https://github.com/ZZY-GraphMiningLab/HeMuc.
INFORMATION SCIENCES
(2023)
Article
Computer Science, Artificial Intelligence
Zhen Wang, Shan-Shan Wang, Lan Bai, Wen-Si Wang, Yuan-Hai Shao
Summary: This article proposes a semisupervised fuzzy clustering method with fuzzy pairwise constraints, which can represent more complex relationships between samples and avoid eliminating fuzzy characteristics. The method solves a nonconvex optimization problem using a modified expectation-maximization algorithm and diagonal block coordinate descent algorithm, and is extended to different metric spaces. Experimental results demonstrate the superior performance of this method.
IEEE TRANSACTIONS ON FUZZY SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Alexander Tornede, Lukas Gehring, Tanja Tornede, Marcel Wever, Eyke Huellermeier
Summary: This paper introduces the problem of meta algorithm selection and presents a general methodological framework and several concrete learning methods. Experimental results show that ensembles of algorithm selectors can significantly outperform single algorithm selectors and have the potential to become the new state of the art in algorithm selection.
Article
Computer Science, Artificial Intelligence
Ahmad Alsahaf, Nicolai Petkov, Vikram Shenoy, George Azzopardi
Summary: This study introduces a novel feature selection framework based on boosting algorithm for selecting informative feature sets in classification problems. Comparative experiments on benchmark datasets show that the proposed method achieves higher accuracy with fewer features on most datasets, and the selected features exhibit lower redundancy.
EXPERT SYSTEMS WITH APPLICATIONS
(2022)
Article
Computer Science, Artificial Intelligence
Xianxian Li, Jing Liu, Songfeng Liu, Jinyan Wang
Summary: This study applies differential privacy techniques to ensemble learning, proposing an algorithm that achieves a balance between privacy protection and prediction accuracy in classification.
Article
Biochemical Research Methods
Qiguo Dai, Zhaowei Wang, Ziqiang Liu, Xiaodong Duan, Jinmiao Song, Maozu Guo
Summary: In this study, a new computational method called ERMDA is proposed to predict potential disease-related miRNAs. The method addresses the challenge of sample imbalance by using a resampling strategy to build balanced training subsets. It extracts miRNA and disease feature representations and applies a feature selection approach to reduce redundancy. Experimental results demonstrate that ERMDA outperforms other methods on testing sets, and case studies confirm its prediction capability for identifying disease-related miRNAs.
BRIEFINGS IN BIOINFORMATICS
(2022)
Article
Biology
Vahid Nosrati, Mohsen Rahmani
Summary: This paper presents a novel framework, named feature-level aggregation-based ensemble based on overlapped feature subspace partitioning (FLAE-OFSP), for microarray data classification. The proposed ensemble generates multiple subsets and applies feature selection algorithms to each subset, and the results are combined into a single ranked list. Evaluation on seven microarray datasets shows significant improvement in runtime and quality results compared to individual methods.
COMPUTERS IN BIOLOGY AND MEDICINE
(2022)
Article
Psychology, Multidisciplinary
Jingfang Liu, Mengshi Shi
Summary: This study uses machine learning technology to detect users with depression by analyzing user-shared content and posting behaviors. A hybrid feature selection and stacking ensemble strategy is proposed to improve the recognition accuracy. The experimental results show that this method achieves a high accuracy of 90.27% in identifying online patients.
FRONTIERS IN PSYCHOLOGY
(2022)
Article
Computer Science, Artificial Intelligence
Yao Zhang, Yingcang Ma
Summary: This paper proposes a non-negative multi-label feature selection method with dynamic graph constraints to address the loss of label information. Experimental results demonstrate the effectiveness of the proposed method.
KNOWLEDGE-BASED SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Christian Bessiere, Clement Carbonnel, Anton Dries, Emmanuel Hebrard, George Katsirelos, Nina Narodytska, Claude-Guy Quimper, Kostas Stergiou, Dimosthenis C. Tsouros, Toby Walsh
Summary: In this paper, we propose a method to learn constraint networks by asking users partial queries. We present an algorithm called QuAcQ2, which can elucidate a constraint of the target network with a logarithmically proportional number of queries to the negative example. Our experiments show that QuAcQ2 requires significantly fewer queries compared to its predecessor, QuAcQ1, for network learning. This research has important implications for improving the efficiency of learning constraint networks.
ARTIFICIAL INTELLIGENCE
(2023)
Article
Computer Science, Information Systems
Mamoon M. Saeed, Rashid A. Saeed, Maha Abdelhaq, Raed Alsaqour, Mohammad Kamrul Hasan, Rania A. Mokhtar
Summary: The 6G era of networks is closely related to intelligent network orchestration and management, which requires the involvement of artificial intelligence, machine learning, and deep learning. This article explores the application of AI in 6G security and presents a redeveloped anomaly detection system based on ensemble learning for 6G networks. The experimental results show that the system achieves high accuracy and low false alarm rate on several datasets.
Article
Geochemistry & Geophysics
Shiguo Chen, Daoqiang Zhang
IEEE GEOSCIENCE AND REMOTE SENSING LETTERS
(2011)
Article
Computer Science, Artificial Intelligence
Fengshan Wang, Daoqiang Zhang
NEURAL PROCESSING LETTERS
(2013)
Correction
Neurosciences
Daoqiang Zhang, Dinggang Shen
Article
Neurosciences
Chong-Yaw Wee, Pew-Thian Yap, Daoqiang Zhang, Kevin Denny, Jeffrey N. Browndyke, Guy G. Potter, Kathleen A. Welsh-Bohmer, Lihong Wang, Dinggang Shen
Article
Neurosciences
Daoqiang Zhang, Dinggang Shen
Article
Neurosciences
Manhua Liu, Daoqiang Zhang, Dinggang Shen
Article
Multidisciplinary Sciences
Daoqiang Zhang, Dinggang Shen
Proceedings Paper
Computer Science, Artificial Intelligence
Linling Zhu, Linsong Miao, Daoqiang Zhang
PATTERN RECOGNITION
(2012)
Proceedings Paper
Computer Science, Artificial Intelligence
Chen Zu, Daoqiang Zhang
PATTERN RECOGNITION
(2012)
Proceedings Paper
Computer Science, Artificial Intelligence
Qimiao Guo, Daoqiang Zhang
PATTERN RECOGNITION
(2012)
Proceedings Paper
Computer Science, Artificial Intelligence
Daoqiang Zhang, Dinggang Shen
MACHINE LEARNING IN MEDICAL IMAGING
(2011)
Proceedings Paper
Computer Science, Artificial Intelligence
Bo Cheng, Daoqiang Zhang, Songcan Chen, Dinggang Shen
MACHINE LEARNING IN MEDICAL IMAGING
(2011)
Proceedings Paper
Computer Science, Information Systems
Chong-Yaw Wee, Pew-Thian Yap, Daoqiang Zhang, Kevin Denny, Lihong Wang, Dinggang Shen
MEDICAL IMAGE COMPUTING AND COMPUTER-ASSISTED INTERVENTION (MICCAI 2011), PT II
(2011)
Proceedings Paper
Computer Science, Theory & Methods
Daoqiang Zhang, Guorong Wu, Hongjun Jia, Dinggang Shen
MEDICAL IMAGE COMPUTING AND COMPUTER-ASSISTED INTERVENTION, MICCAI 2011, PT III
(2011)
Proceedings Paper
Computer Science, Theory & Methods
Daoqiang Zhang, Dinggang Shen
MULTIMODAL BRAIN IMAGE ANALYSIS
(2011)
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)