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International Symposium on Future I&C for Nuclear Power Plants（ISOFIC2014）

作者： Seung Geun Kim Poong Hyun Seong Dept.of Nuclear and Quantum Engineering Korea Advanced Institute of Science and Technology

The occurrence of severe accident in Fukushima reminded that getting information about severe accident scenario is important in order to achieve ultimate goal of *** this research,methods for severe accident occurrence time prediction in various LOCA scenarios by using support vector classification and support vector regression were *** location and size identifications for prior step were properly conducted and time when maximum core temperature exceeds 1200 degree Celsius,reactor vessel failure time and containment failure time were predicted with acceptable errors.

关键词： Severe Accident LOCA support vector classification support vector Regression

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Statistical Rates of Convergence for Functional Partially Linear support vector Machines for classification

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JOURNAL OF MACHINE LEARNING RESEARCH 2022年第1期23卷 1-24页

作者： Zhang, Yingying Zhao, Yan-Yong Lian, Heng East China Normal Univ KLATASDS MOE Acad Stat & Interdisciplinary Sci Shanghai Peoples R China Nanjing Audit Univ Sch Stat & Data Sci Nanjing Peoples R China City Univ Hong Kong Shenzhen Res Inst Shenzhen Peoples R China City Univ Hong Kong Dept Math Kowloon Tong Hong Kong Peoples R China

In this paper, we consider the learning rate of support vector machines with both a func-tional predictor and a high-dimensional multivariate vectorial predictor. Similar to the literature on learning in reproducing kernel Hilbert spaces, a source condition and a capac-ity condition are used to characterize the convergence rate of the estimator. It is highly non-trivial to establish the possibly faster rate of the linear part. Using a key basic in-equality comparing losses at two carefully constructed points, we establish the learning rate of the linear part which is the same as if the functional part is known. The proof relies on empirical processes and the Rademacher complexity bound in the semi-nonparametric setting as analytic tools, Young's inequality for operators, as well as a novel "approximate convexity" assumption.

关键词： Convergence rate Prediction risk Rademacher complexity support vector classification

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Statistical rates of convergence for functional partially linear support vector machines for classification

The Journal of Machine Learning Research

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The Journal of Machine Learning Research 2022年第1期23卷 7019-7042页

作者： Yingying Zhang Yan-Yong Zhao Heng Lian Academy of Statistics and Interdisciplinary Sciences KLATASDS-MOE East China Normal University Shanghai China School of Statistics and Data Science Nanjing Audit University Nanjing China City University of Hong Kong Shenzhen Research Institute Shenzhen China and Department of Mathematics City University of Hong Kong Kowloon Tong Hong Kong China

In this paper, we consider the learning rate of support vector machines with both a functional predictor and a high-dimensional multivariate vectorial predictor. Similar to the literature on learning in reproducing kernel Hilbert spaces, a source condition and a capacity condition are used to characterize the convergence rate of the estimator. It is highly non-trivial to establish the possibly faster rate of the linear part. Using a key basic inequality comparing losses at two carefully constructed points, we establish the learning rate of the linear part which is the same as if the functional part is known. The proof relies on empirical processes and the Rademacher complexity bound in the semi-nonparametric setting as analytic tools, Young's inequality for operators, as well as a novel "approximate convexity" assumption.

关键词： convergence rate prediction risk rademacher complexity support vector classification

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Ground-level ozone prediction by support vector machine approach with a cost-sensitive classification scheme

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SCIENCE OF THE TOTAL ENVIRONMENT 2008年第2-3期395卷 109-116页

作者： Lu, Wei-Zhen Wang, Dong City Univ Hong Kong Dept Bldg & Construct Kowloon Peoples R China

For ground-level ozone (03) prediction, a predictive model, with reliable performance not only on non-polluted days but, more importantly, on polluted days, is favored by public authorities to issue alerts, so that concerned citizens and industrial organizations could take precautions to avoid exposure and reduce harmful emissions. However, the class imbalance problem, i.e., in some collected field data, number Of O-3 polluted days are much smaller than that of non-polluted days, will deteriorate the model performance on minority class-O-3 polluted days. Despite support vector machine (SVM) obtaining promising results in air quality prediction, in this study, a cost-sensitive classification scheme is proposed for the standard support vector classification model (S-SVC) in order to investigate whether the class imbalance plagues S-SVC. The S-SVC with such scheme is named as CS-SVC. Experiments on imbalanced data sets collected from two air quality monitoring sites in Hong Kong show that 1) S-SVC is still sensitive to class imbalance problem;2) compared with S-SVC, CS-SVC effectively avoids class imbalance problem with lower percentage of false negative on O-3 polluted days but with higher percentage of false positive on non-polluted days;3) compared with both S-SVC and CS-SVC, support vector regression model (SVR), after converting its output to binary one, only has similar performance with S-SVC, which indicates class imbalance problem also impairs the regressor model. From point of protecting public health, CS-SVC, which less likely misses to forecast O-3 polluted days, is recommended here. (c) 2008 Elsevier B.V. All rights reserved.

关键词： ozone support vector classification class imbalance problem cost-sensitive classification support vector regression

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Stable biomarker screening and classification by subsampling-based sparse regularization coupled with support vector machines in metabolomics

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CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS 2017年 160卷 22-31页

作者： Fu, Guang-Hui Zhang, Bing-Yang Kou, He-Dan Yi, Lun-Zhao Kunming Univ Sci & Technol Sch Sci Kunming 650500 Peoples R China Kunming Univ Sci & Technol Yunnan Food Safety Res Inst Kunming 650500 Peoples R China

The partial least squares-discriminant analysis (PLS-DA) is the most widely used statistical tool to perform classification and biomarker screening in metabolomics. However, the PLS-DA tends to overfit the data, and the selection of biomarkers is often unstable because of the disturbance of uninformative variables in principal components. In this paper, we propose an algorithm for performing stable biomarker screening and for seeking the optimal generalization performance, in which the biomarker identification is based on sparse regularization variable selection in combination with subsampling (SRS), and the classification is subsequently performed by a linear support vector machine (SVM) classifier in the selected-variable space to obtain the maximum classification accuracy. Two metabolomics datasets measured by gas chromatography-mass spectrometry are employed to evaluate the performance of the proposed SRS-SVM algorithm, and the comparison with existing related algorithms is given. The result shows that the SRS-SVM algorithm outperforms the PLS-DA and is competitive with other related algorithms in terms of prediction classification accuracy measured by both internal and external validation. Furthermore, the selection of candidate biomarkers is quite stable by the SRS-SVM algorithm, and it can be an alternative and competitive method for the analysis of metabolomics data. The R code for implementing the SRS-SVM algorithm is available in the Electronic supplementary material.

关键词： Sparse regularization with subsampling Stable biomarker screening support vector classification LASSO Sparse partial least squares

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Automated classification of Epiphyses in the Distal Radius and Ulna using a support vector Machine

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JOURNAL OF FORENSIC SCIENCES 2016年第2期61卷 409-414页

作者： Wang, Ya-hui Liu, Tai-ang Wei, Hua Wan, Lei Ying, Chong-liang Zhu, Guang-you Minist Justice Inst Forens Sci Shanghai Key Lab Forens Med 1347 West Guangfu Rd Shanghai 200063 Peoples R China Shanghai Univ Sch Mat Sci & Engn 99 Shangda Rd Shanghai Peoples R China Inst Shanghai Huayi Forens Sci 1277 Dingxi Rd Shanghai Peoples R China

The aim of this study was to automatically classify epiphyses in the distal radius and ulna using a support vector machine (SVM) and to examine the accuracy of the epiphyseal growth grades generated by the support vector machine. X-ray images of distal radii and ulnae were collected from 140 Chinese teenagers aged between 11.0 and 19.0 years. Epiphyseal growth of the two elements was classified into five grades. Features of each element were extracted using a histogram of oriented gradient (HOG), and models were established using support vector classification (SVC). The prediction results and the validity of the models were evaluated with a cross-validation test and independent test for accuracy (P-A). Our findings suggest that this new technique for epiphyseal classification was successful and that an automated technique using an SVM is reliable and feasible, with a relative high accuracy for the models.

关键词： forensic science forensic anthropology distal radius distal ulna support vector classification support vector machine histogram of oriented gradient

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Linear Multi-class classification support vector Machine

Linear Multi-class Classification Support Vector Machine

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20th International Conference on Multiple Criteria Decision Making

作者： Xu, Yan Shao, Yuanhai Tian, Yingjie Deng, Naiyang China Agr Univ Coll Sci Beijing 100083 Peoples R China Univ Sci & Technol Coll Applicat & Sci Beijing 100083 Peoples R China Grad Univ CAS Beijing 100080 Peoples R China

ISBN: (纸本)9783642022975

support vector Machines (SVMs) for classification have been shown to be promising classification tools in many real-world problems. How to effectively extend binary SVC to multi-class classification is still an on-going research issue. In this article, instead of solving quadratic programming (QP) in algorithm in [1], utilizing a linear function in the objective function a linear programming (LP) problem is introduced in our algorithm,thus leading to a new algorithm for multi-class problem named linear multi-class classification support vector machine. Numerical experiments on artificial data sets and benchmark data sets show that the proposed method is comparable to algorithm [1] in errors, while considerably ten times faster and the same robustness.

关键词： support vector classification Linear programming Multi-class classification

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Spectral signature classification using a support vector classifier for real-time instrumentation

Spectral signature classification using a support vector cla...

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24th IEEE Instrumentation and Measurement Technology Conference

作者： Nemati, S. Yeary, M. Yu, T. -Y. Wang, Y. Zhai, Y. Fagg, A. H. Univ Oklahoma Sch Elect & Comp Engn 202 W BoydRoom 219 Norman OK 73019 USA Univ Oklahoma Sch Comp Sci Norman OK USA

ISBN: (纸本)9781424405886

The research WSR-88D (weather surveillance radar) locally operated by the National Severe Storm Laboratory (NSSL) in Norman has the unique capability of collecting massive volumes of Level I time series data over many hours which provides a rich environment for evaluating our new post-processing algorithms. In this work, a support vector Machine (SVM) classifier is employed to identify tornado vortices based on their characteristic Doppler spectra and eigen analysis technique. A SVM-based classifier evades the pitfalls of the traditional statistical learning algorithms, such as neural networks, by setting up a convex optimization problem with a single global minimum. In addition, through the use of kernels and nonlinear mapping to higher dimensional spaces, the SVM classifier is able to effectively handle nonlinear classification problems. Finally, the SVM classifier has the added advantage of reducing overfitting by constructing a maximum margin separating hyperplane in a higher dimensional feature space which ensures a small generalization error bound [1].

关键词： radar measurements sensor networks remote sensing spectral signature calculations WSR-88D (KOUN) support vector classification eigen analysis and real-time sensor instrumentation

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Prediction of breast cancer using metaheuristic-driven ensemble learning: A novel classification approach

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MATHEMATICS AND COMPUTERS IN SIMULATION 2025年 236卷 29-51页

作者： Yang, Zheng Zhou, Rui Qu, Huairong Liu, Liang Wu, Qingbin Chinese Univ Hong Kong Shenzhen 518060 Guangdong Peoples R China Natl Hlth Data Inst Intelligent Imaging Business Shenzhen 518038 Guangdong Peoples R China Chinese Univ Hong Kong Network Ctr DeptAffiliated Hosp 2 Shenzhen & Longgang Dist Peoples Hosp Shenzhen Sch Med Shenzhen 518172 Guangdong Peoples R China Nanjing Med Univ Peoples Hosp Lianyungang 1 Informat Dept Kangda Coll Lianyungang 222061 Jiangsu Peoples R China Guangdong Prov Peoples Hosp Informat Management Dept Guangzhou Peoples R China Jinan Univ Affiliated Shunde Hosp Foshan 528305 Peoples R China

Breast cancer remains one of the most common cancers among women, with a high mortality rate. Early diagnosis is crucial for effective treatment and prevention. This study introduces an innovative approach to breast cancer prediction, integrating support vector classification and Histogram Gradient Boosting classification models with a novel ensemble method using bagging. To further enhance predictive accuracy, three new metaheuristic optimization algorithms (Mother Optimization Algorithm, Osprey Optimization Algorithm, and Puma Optimization Algorithm) are employed. The study rigorously applies feature selection techniques and k-fold cross-validation to ensure optimal results. The novelty lies in the cooperative use of these reliable classification models with advanced metaheuristic optimizers and an ensemble strategy, leading to superior performance in breast cancer prediction. The Boosting Algorithm Puma Optimization Algorithm model, optimized with the Puma Optimization Algorithm, achieved exceptional classification performance, with 0.9606 for malignant cases and 0.9760 for benign cases, supported by an Metahueristic Algorithm classification of 0.9368. This demonstrates the model's high accuracy and reliability in clinical diagnosis, making a significant contribution to healthcare by optimizing machine learning models for more accurate and trustworthy predictions.

关键词： Breast cancer classification Ensemble support vector classification Histogram Gradient Boosting classification Metahueristic algorithm

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MaxMin-L2-SVC-NCH: A novel approach for support vector classifier training and parameter selection

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NEUROCOMPUTING 2025年 623卷

作者： Luo, Linkai Yang, Qiaoling Peng, Hong Wang, Yiding Chen, Ziyang Xiamen Univ Dept Automat Xiamen 361102 Peoples R China Xiamen Key Lab Big Data Intelligent Anal & Decis M Xiamen 361102 Peoples R China

The selection of Gaussian kernel parameters plays an important role in the applications of support vector classification (SVC). A commonly used method is the k-fold cross validation with grid search (CV), which is extremely time-consuming because it needs to train a large number of SVC models. In this paper, a new approach is proposed to train SVC and optimize the selection of Gaussian kernel parameters. We first formulate the training and the parameter selection of SVC as a minimax optimization problem named as MaxMin-L2-SVCNCH, in which the minimization problem is an optimization problem of finding the closest points between two normal convex hulls (L2-SVC-NCH) while the maximization problem is an optimization problem of finding the optimal Gaussian kernel parameters. A lower time complexity can be expected in MaxMin-L2-SVC-NCH because CV is not needed. We then propose a projected gradient algorithm (PGA) for the training of L2-SVC-NCH. It is revealed that the famous sequential minimal optimization (SMO) algorithm is a special case of the PGA. Thus, the PGA can provide more flexibility than the SMO. Furthermore, the solution of the maximization problem is done by a gradient ascent algorithm with dynamic learning rate. The comparative experiments between MaxMin-L2-SVC-NCH and the previous best approaches on public datasets show that MaxMin-L2-SVC-NCH greatly reduces the number of models to be trained while maintaining competitive test accuracy. These findings indicate that MaxMin-L2-SVC-NCH is a better choice for SVC tasks.

关键词： support vector classification The selection of kernel parameters Minimax optimization Closest points Normal convex hull Projected gradient algorithm

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