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Research on Star/Galaxy Classification Based on Stacking Ensemble Learning

Chinese Astronomy and Astrophysics 2020年第3期44卷 345-355页

作者： Chao, L.I. Wen-hui, ZHANG Ran, L.I. Jun-yi, W.A.N.G. Ji-ming, L.I.N. College of Information and Communication Engineering Guilin University of Electronic Technology Guilin 541004 Key Laboratory of Education Ministry for Cognitive Radio and Information Processing Guilin University of Electronic Technology Guilin 541004 Guangxi Cooperative Innovation Center of Cloud Computing and Big Data Guilin University of Electronic Technology Guilin 541004 Guangxi College and University Key Laboratory for Cloud Computing and Complex Systems Guilin University of Electronic Technology Guilin 541004 Guangxi Key Laboratory for Wireless Wideband Communication and Signal Processing Guilin University of Electronic Technology Guilin 541004 Guangxi College and University Key Laboratory for Satellite Navigation and Position Sensing Guilin 541004

Machine learning has achieved great success in many areas today, but the forecast effect of machine learning often depends on the specific problem. An ensemble learning forecasts results by combining multiple base classifiers. Therefore, its ability to adapt to various scenarios is strong, and the classification accuracy is high. In response to the low classification accuracy of the darkest source magnitude set of stars/galaxies in the Sloan Digital Sky Survey (SDSS), a star/galaxy classification algorithm based on the stacking ensemble learning is proposed in this paper. The complete photometric data set is obtained from the SDSS data Release (DR) 7, and divided into the bright source magnitude set, dark source magnitude set, and darkest source magnitude set according to the stellar magnitude. Firstly, the 10-fold nested cross-validation method is used for the darkest source magnitude set, then the Support Vector Machine (SVM), Random Forest (RF), and eXtreme Gradient Boosting (XGBoost) algorithms are used to establish the base-classifier model;the Gradient Boosting Decision Tree (GBDT) is used as the meta-classifier model. Finally, based on the classification accuracy of galaxies and other indicators, the classification results are analyzed and compared with the results obtained by the Function Tree (FT), SVM, RF, GBDT, Stacked Denoising Autoencoders (SDAE), Deep Belief Nets (DBN), and Deep Perception Decision Tree (DPDT) models. The experimental results show that the stacking ensemble learning model has improved the classification accuracy of galaxies in the darkest source magnitude set by nearly 10% compared to the function tree algorithm. Compared with other traditional machine learning algorithm, stronger lifting algorithm, and deep learning algorithm, the stacking ensemble learning model also has different degrees of improvement. © 2020

关键词： Galaxies: fundamental parameters methods: data analysis Stars: fundamental parameters techniques: photometric

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Two-Month cognitive Changes Enhance Prediction of Nonremission in Clinical High-Risk Individuals

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Biological Psychiatry 2025年第2期98卷 175-185.0页

作者： Zhang, TianHong Wei, YanYan Tang, XiaoChen Xu, LiHua Cui, HuiRu Hu, YeGang Liu, HaiChun Wang, ZiXuan Chen, Tao Tang, YingYing Yi, ZhengHui Li, ChunBo Wang, JiJun Shanghai Mental Health Center Shanghai Jiaotong University School of Medicine Shanghai Engineering Research Center of Intelligent Psychological Evaluation and Intervention Shanghai Key Laboratory of Psychotic Disorders Shanghai China Department of Automation Shanghai Jiao Tong University Shanghai China Shanghai Xinlianxin Psychological Counseling Center Shanghai China Big Data Research Laboratory University of Waterloo Waterloo ON Canada Labor and Worklife Program Harvard University Cambridge Massachusetts United States Center for Excellence in Brain Science and Intelligence Technology Chinese Academy of Sciences Shanghai China Institute of Psychology and Behavioral Science Shanghai Jiao Tong University Shanghai China

Background: Longitudinal changes in cognitive function may be crucial in predicting clinical outcomes in clinical high-risk (CHR) individuals. In this study, we aimed to investigate the predictive value of baseline cognitive impairment and short-term cognitive changes for nonremission and conversion to psychosis in individuals at CHR for psychosis compared with healthy control individuals (HCs). Methods: This study used a multiple-group prospective design with a 3-year follow-up. CHR individuals and HCs were assessed at baseline and at a 2-month follow-up. Neuropsychological performance was evaluated using the Chinese version of the MATRICS (Measurement and Treatment Research to Improve Cognition in Schizophrenia) Consensus cognitive Battery. Results: The study included 310 CHR individuals and 93 HCs. Significant improvements in predicting nonremission in CHR individuals were observed when incorporating cognitive changes over 2 months (area under the receiver operating characteristic curve [AUC] for baseline cognition, 0.690;AUC for changes, 0.819;z = 3.365, p < .001). Key predictors included the Hopkins Verbal Learning Test–Revised (β = 0.083, p = .003), Wechsler Memory Scale-III spatial span (β = 0.330, p < .001), and Brief Visuospatial Memory Test–Revised (β = 0.127, p < .001). Conversely, predicting conversion to psychosis showed no significant difference between baseline and 2-month cognitive changes (AUC for baseline cognition, 0.667;AUC for changes, 0.666;z = 0.021, p = .242). Conclusions: The findings underscore the importance of dynamic cognitive monitoring in CHR individuals. Short-term cognitive changes significantly enhanced the prediction of nonremission but did not add predictive value for conversion to psychosis beyond baseline assessments. Specific cognitive domains, such as verbal learning and working memory, were particularly valuable for predicting clinical outcomes. © 2025 Society of Biological Psychiatry

关键词： Cognition Functional outcome Prodromal psychosis Remission Transition Ultra-high risk

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A multi-strategy improved crow search algorithm for multi-level thresholding image segmentation

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Scientific Reports 2025年第1期15卷 1-49页

作者： Zhang, Xiaoping Huang, Chengliang Gui, Weixia School of Computer Electronics and Information Guangxi University Nanning 530004 China Information Technology Management Department Toronto Metropolitan University Toronto M5B 2K3 Canada School of Big Data and Artificial Intelligence Guangxi University of Finance and Economics Nanning 530004 China Guangxi Key Laboratory of Big Data in Finance and Economics Guangxi University of Finance and Economics Nanning 530004 China

The standard crow search algorithm suffers from low convergence accuracy, insufficient stability, and susceptibility to getting stuck in local optima. To tackle these formidable challenges, this paper proposes a novel multi-strategy improved crow search algorithm (MSICSA) specifically designed for multi-level image segmentation. The proposed approach incorporates three key enhancements: firstly, opposition-based learning (OBL) is utilized to improve the quality of initial solutions within MSICSA;secondly, an adaptive awareness probability mechanism is introduced to better balance the trade-off between exploration and exploitation;lastly, two differential mutation operators are developed to enhance global search capabilities, increase population diversity, and reduce the risk of converging on local optima. To validate the performance of the proposed algorithm, two sets of experiments are conducted. In the first set of experiments, CEC 2020 benchmark test functions are selected to compare the performance of MSICSA with other group intelligent optimization algorithms. In the second set of experiments, Otsu’s method and fuzzy entropy are employed as objective functions for performing multilevel threshold segmentation on twelve grayscale images. The experimental results demonstrate that MSICSA outperforms seven comparison algorithms in terms of both convergence speed and segmentation quality. © The Author(s) 2025.

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Comprehensive integrated analysis of MR and DCE-MR radiomics models for prognostic prediction in nasopharyngeal carcinoma

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Visual Computing for Industry,Biomedicine,and Art 2023年第1期6卷 329-342页

作者： Hailin Li Weiyuan Huang Siwen Wang Priya SBalasubramanian Gang Wu Mengjie Fang Xuebin Xie Jie Zhang Di Dong Jie Tian Feng Chen Beijing Advanced Innovation Center for Big Data-Based Precision Medicine School of Medicine and EngineeringBeihang UniversityBeijing 100191China CAS Key Laboratory of Molecular Imaging Institute of AutomationChinese Academy of SciencesBeijing 100190China Department of Radiology Hainan General Hospital(Hainan Affiliated Hospital of Hainan Medical University)HaikouHainan 570311China School of Artificial Intelligence University of Chinese Academy of SciencesBeijing 100049China Department of Psychiatry Weill Cornell MedicineNew YorkNY 10065USA Department of Radiotherapy Hainan General Hospital(Hainan Affiliated Hospital of Hainan Medical University)HaikouHainan 570311China Department of Radiology Kiang Wu HospitalSanto AntónioMacao 999078China Department of Radiology Zhuhai People’s Hospital(Zhuhai Hospital Affiliated With Jinan University)ZhuhaiGuangdong 519000China Engineering Research Center of Molecular and Neuro Imaging of Ministry of Education School of Life Science and TechnologyXidian UniversityXi’anShaanxi 710126China Zhuhai Precision Medical Center Zhuhai People’s HospitalZhuhaiGuangdong 519000China.

Although prognostic prediction of nasopharyngeal carcinoma (NPC) remains a pivotal research area, the role of dynamic contrast-enhanced magnetic resonance (DCE-MR) has been less explored. This study aimed to investigate the role of DCR-MR in predicting progression-free survival (PFS) in patients with NPC using magnetic resonance (MR)- and DCE-MR-based radiomic models. A total of 434 patients with two MR scanning sequences were included. The MR- and DCE-MR-based radiomics models were developed based on 289 patients with only MR scanning sequences and 145 patients with four additional pharmacokinetic parameters (volume fraction of extravascular extracellular space (ve), volume fraction of plasma space (vp), volume transfer constant (Ktrans), and reverse reflux rate constant (kep) of DCE-MR. A combined model integrating MR and DCE-MR was constructed. Utilizing methods such as correlation analysis, least absolute shrinkage and selection operator regression, and multivariate Cox proportional hazards regression, we built the radiomics models. Finally, we calculated the net reclassification index and C-index to evaluate and compare the prognostic performance of the radiomics models. Kaplan-Meier survival curve analysis was performed to investigate the model’s ability to stratify risk in patients with NPC. The integration of MR and DCE-MR radiomic features significantly enhanced prognostic prediction performance compared to MR- and DCE-MR-based models, evidenced by a test set C-index of 0.808 vs 0.729 and 0.731, respectively. The combined radiomics model improved net reclassification by 22.9%-52.6% and could significantly stratify the risk levels of patients with NPC (p = 0.036). Furthermore, the MR-based radiomic feature maps achieved similar results to the DCE-MR pharmacokinetic parameters in terms of reflecting the underlying angiogenesis information in NPC. Compared to conventional MR-based radiomics models, the combined radiomics model integrating MR and DCE-MR showed

关键词： Dynamic contrast-enhanced magnetic resonance imaging Magnetic resonance imaging Radiomics Prognostic prediction

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MAVEN-ERE: A Unified Large-scale dataset for Event Coreference, Temporal, Causal, and Subevent Relation Extraction

arXiv

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arXiv 2022年

作者： Wang, Xiaozhi Chen, Yulin Ding, Ning Peng, Hao Wang, Zimu Lin, Yankai Han, Xu Hou, Lei Li, Juanzi Liu, Zhiyuan Li, Peng Zhou, Jie Department of Computer Science and Technology BNRist China Shenzhen International Graduate School China THU-Siemens Ltd. China Joint Research Center for Industrial Intelligence and IoT China Tsinghua University Beijing China Xi’an Jiaotong-Liverpool University Suzhou China Gaoling School of Artificial Intelligence Renmin University of China Beijing China Beijing Key Laboratory of Big Data Management and Analysis Methods Beijing China Pattern Recognition Center WeChat AI Tencent Inc China

The diverse relationships among real-world events, including coreference, temporal, causal, and subevent relations, are fundamental to understanding natural languages. However, two drawbacks of existing datasets limit event relation extraction (ERE) tasks: (1) Small scale. Due to the annotation complexity, the data scale of existing datasets is limited, which cannot well train and evaluate data-hungry models. (2) Absence of unified annotation. Different types of event relations naturally interact with each other, but existing datasets only cover limited relation types at once, which prevents models from taking full advantage of relation interactions. To address these issues, we construct a unified large-scale human-annotated ERE dataset MAVEN-ERE with improved annotation schemes. It contains 103, 193 event coreference chains, 1, 216, 217 temporal relations, 57, 992 causal relations, and 15, 841 subevent relations, which is larger than existing datasets of all the ERE tasks by at least an order of magnitude. Experiments show that ERE on MAVEN-ERE is quite challenging, and considering relation interactions with joint learning can improve performances. The dataset and source codes can be obtained from https://***/THU-KEG/MAVEN-ERE. Copyright © 2022, The Authors. All rights reserved.

关键词： Extraction

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RadioGAT: A Joint Model-based and data-driven Framework for Multi-band Radiomap Reconstruction via Graph Attention Networks

arXiv

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arXiv 2024年

作者： Li, Xiaojie Zhang, Songyang Li, Hang Li, Xiaoyang Xu, Lexi Xu, Haigao Mei, Hui Zhu, Guangxu Qi, Nan Xiao, Ming Shenzhen Research Institute of Big Data The Chinese University of Hong Kong-Shenzhen Guangdong China College of Physics Nanjing University of Aeronautics and Astronautics Nanjing210016 China Department of Electrical and Computer Engineering University of Louisiana at Lafayette LafayetteLA70503 United States Research Institute China United Network Communications Corporation Beijing100048 China China Mobile Communications Group Jiangxi Co. Ltd 330038 China Key Laboratory of Dynamic Cognitive System of Electromagnetic Spectrum Space Nanjing University of Aeronautics and Astronautics Nanjing210016 China State Key Laboratory of Integrated Services Networks Xidian University Xi'An710071 China Department of Information Science and Engineering School of Electrical Engineering of KTH Royal Institute of Technology Stockholm Sweden

Multi-band radiomap reconstruction (MB-RMR) is a key component in wireless communications for tasks such as spectrum management and network planning. However, traditional machine-learning-based MB-RMR methods, which rely heavily on simulated data or complete structured ground truth, face significant deployment challenges. These challenges stem from the differences between simulated and actual data, as well as the scarcity of real-world measurements. To address these challenges, our study presents RadioGAT, a novel framework based on Graph Attention Network (GAT) tailored for MBRMR within a single area, eliminating the need for multiregion datasets. RadioGAT innovatively merges model-based spatial-spectral correlation encoding with data-driven radiomap generalization, thus minimizing the reliance on extensive data sources. The framework begins by transforming sparse multiband data into a graph structure through an innovative encoding strategy that leverages radio propagation models to capture the spatial-spectral correlation inherent in the data. This graph-based representation not only simplifies data handling but also enables tailored label sampling during training, significantly enhancing the framework's adaptability for deployment. Subsequently, The GAT is employed to generalize the radiomap information across various frequency bands. Extensive experiments using raytracing datasets based on real-world environments have demonstrated RadioGAT's enhanced accuracy in supervised learning settings and its robustness in semi-supervised scenarios. These results underscore RadioGAT's effectiveness and practicality for MBRMR in environments with limited data availability. Copyright © 2024, The Authors. All rights reserved.

关键词： Encoding (symbols)

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Automatic Labeling for Gene-Disease Associations through Distant Supervision 14

Automatic Labeling for Gene-Disease Associations through Dis...

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14th IEEE International Conference on Intelligent Systems and Knowledge Engineering, ISKE 2019

作者： Teng, Fei Bai, Meng Li, Tianrui Institute of Artificial Intelligence National Engineering Laboratory Integrated Transportation Big Data Application Technology School of Information Science Technology Southwest Jiaotong University Chengdu China

ISBN: (纸本)9781728123486

Associating genes with diseases is a fundamental challenge in human health with applications of understanding disease properties and developing precision medicine. Over the past decades, biomedical articles increase explosively, which contain a great number of gene-disease associations (GDAs). Association extraction requires annotated corpus of high accuracy, but manual labeling is time consuming and labor intensive. This paper proposes a distant supervision-based method, to automatically label corpus for GDAs extraction. Compared with the manually annotated gold corpus, the automatic labeled corpus has much larger scale and better quality. It improves the performance of state-of-the-art extraction models, with AUC of 0.96, and F1 of 90%. To the best of our knowledge, this is the first study of automatic labeling GDAs in the field of precision medicine. We extracted GDAs using new corpora from 115,261 PubMed abstracts about 29 lung cancers, and finally discovered 296 new genes/proteins related to lung cancers. These findings indicate new directions for drug design. © 2019 IEEE.

关键词： Genes

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Deep learning-based hyperspectral and multispectral fusion techniques: Review, optimization, and perspectives

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Information Fusion 2025年 124卷

作者： Sen Luo Yurong Qian Lu Bai Yingying Fan Yuanxu Wang WeiQuan Kong Xinjiang Engineering Research Center of Big Data and Intelligent Software School of Software Xinjiang University Urumqi 830091 China Joint International Research Laboratory of Silk Road Multilingual Cognitive Computing Xinjiang University Urumqi 830046 China School of Computer Science and Technology Xinjiang University Urumqi 830046 China

Hyperspectral and Multispectral Image Fusion (HMIF) aim to acquire high spatial and spectral resolution images, which overcomes the inherent limitations of a single imaging device and effectively extends the applicability of spectral images on various downstream tasks. In recent years, Deep Learning (DL)-based image fusion technology breaks through the many limitations of traditional methods, rapidly develops a variety of neural network architectures and improvement strategies, and gradually overcomes a series of practical difficulties. However, a comprehensive overview of these deep learning architectures and various improvement techniques is still lacking. In this paper, we present a comprehensive review of deep learning techniques on HMIF, organize the fusion paradigms according to different researchers’ understanding and hypotheses of the problem, and sort out the common optimization strategies of neural network models, such as attention mechanism, multi-feature learning, combined with the variational and unmixing models of deep learning. In addition, we also provide a detailed compendium of the practical challenges and corresponding solution strategies for the first time, which involves the spatial-temporal misregistration, generalization enhancement and high-magnification fusion problems. Finally, we sort out and categorize the commonly used evaluation metrics for fused datasets, and discuss the potential research directions in HMIF. Through these works, we hope to provide a quick and clear reference for aspiring researchers in this area.

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Structure-Based Knowledge Tracing: An Influence Propagation View

Structure-Based Knowledge Tracing: An Influence Propagation ...

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IEEE International Conference on data Mining (ICDM)

作者： Shiwei Tong Qi Liu Wei Huang Zhenya Hunag Enhong Chen Chuanren Liu Haiping Ma Shijin Wang Anhui Province Key Laboratory of Big Data Analysis and Application School of Data Science & School of Computer Science and Technology University of Science and Technology of China Anhui Province Key Laboratory of Big Data Analysis and Application School of Data Science &School of Computer Science and Technology University of Science and Technology of China CN Anhui Province Key Laboratory of Big Data Analysis and Application School of Data Science &School of Computer Science and Technology University of Science and Technology of China Business Analytics and Statistics University of Tennessee TN Business Analytics and Statistics University of Tennessee Anhui University CN iFLYTEK Research iFLYTEK CO. LTD CN State Key Laboratory of Cognitive Intelligence iFLYTEK Research iFLYTEK CO. LTD.

ISBN: (数字)9781728183169

ISBN: (纸本)9781728183176

Knowledge Tracing (KT) is a fundamental but challenging task in online education that traces learners' evolving knowledge states. Much attention has been drawn to this area and several works such as Bayesian and Deep Knowledge Tracing have been proposed. Recent works have explored the value of relations among concepts and proposed to introduce knowledge structure into KT tasks. However, the propagated influence among concepts, which has been shown to be a key factor in human learning by the educational theories, is still under-explored. In this paper, we propose a new framework called Structure-based Knowledge Tracing (SKT), which exploits the multiple relations in knowledge structure to model the influence propagation among concepts. In the SKT framework, we consider both the temporal effect on the exercising sequence and the spatial effect on the knowledge structure. We take advantages of two novel formulations in modeling the influence propagation on the knowledge structure with multiple relations. For undirected relations such as similarity relations, the synchronization propagation method is adopted, where the influence propagates bidirectionally between neighbor concepts. For directed relations such as prerequisite relations, the partial propagation method is applied, where the influence can only unidirectionally propagate from a predecessor to a successor. Meanwhile, we employ the gated functions to update the states of concepts temporally and spatially. We demonstrate the effectiveness and interpretability of SKT with extensive experiments.

关键词： Neural networks Education Logic gates Synchronization Mathematical model data mining Task analysis

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DWMFusion: A lightweight medical image fusion network based on discrete wavelet convolution and selective state space models

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Expert Systems with Applications 2025年 286卷

作者： Fan, Chao Yun, Mengyang Chen, Li Zhao, Huijun Peng, Bingcheng School of Artificial Intelligence and Big Data Henan University of Technology Henan Province Zhengzhou City China Ministry of Education Key Laboratory of Grain Information Processing and Control Henan Province Zhengzhou City China School of Information Science and Engineering Henan University of Technology Henan Province Zhengzhou City450001 China

Multimodal medical image fusion improves the accuracy of clinical diagnosis by integrating medical imaging information from different sensors into unified and complementary multi-scale fused information images. However, existing fusion methods still face challenges in capturing modality-specific features, balancing local and global contextual features, and preserving complementary information from different representations. To overcome these shortcomings, this study proposes a lightweight feature complementary fusion network(DWMFusion) to achieve effective fusion of functional and structural medical images under lightweight premise. Specifically, a cross-channel feature extraction unit based on discrete wavelet transform (DWT) and convolutional coupling structure is designed to enhance the modal intrinsic frequency domain features. Meanwhile, a visual state space block (VSSB) and a contextual modeling structure with multiple layers of Mamba are introduced to balance local details with global semantics and reduce computational redundancy. In the image fusion stage, a multilevel cross-fusion unit (CFFM) that introduces position coding is designed to realize deep cross-modal spatio-temporal feature interaction. A joint loss function incorporating wavelet decomposition properties is proposed to effectively adaptively constrain the frequency domain consistency and maintain the structural information. Validation on the Harvard Medical School database shows that DWMFusion is capable of generating visually satisfactory fusion images. And compared with other methods, it improves the SD metrics by an average of 17.59%, the SF metrics by an average of 17.64%, and the AG metrics by an average of 19.96% on the three datasets. These results confirm the robustness and adaptability of the model. © 2025 Elsevier Ltd

关键词： Wavelet decomposition

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