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A second-order cone programming formulation for nonparallel hyperplane support vector machine

EXPERT SYSTEMS WITH APPLICATIONS 2016年 54卷 95-104页

作者： Carrasco, Miguel Lopez, Julio Maldonado, Sebastian Univ Los Andes Fac Ingn & Ciencias Aplicadas Mons Alvaro del Portillo 12455 Santiago Chile Univ Diego Portales Fac Ingn Ejercito 441 Santiago Chile

Expert systems often rely heavily on the performance of binary classification methods. The need for accurate predictions in artificial intelligence has led to a plethora of novel approaches that aim at correctly predicting new instances based on nonlinear classifiers. In this context, support vector Machine (SVM) formulations via two nonparallel hyperplanes have received increasing attention due to their superior performance. In this work, we propose a novel formulation for the method, Nonparallel Hyperplane SVM. Its main contribution is the use of robust optimization techniques in order to construct nonlinear models with superior performance and appealing geometrical properties. Experiments on benchmark datasets demonstrate the virtues in terms of predictive performance compared with various other SVM formulations. Managerial insights and the relevance for intelligent systems are discussed based on the experimental outcomes. (C) 2016 Elsevier Ltd. All rights reserved.

关键词： support vector classification Nonparallel hyperplane SVM Second-order cone programming

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RETRACTED: A secured biomedical image processing scheme to detect pneumonia disease using dynamic learning principles (Retracted Article)

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CONCURRENT ENGINEERING-RESEARCH AND APPLICATIONS 2022年第3期30卷 245-252页

作者： Nanammal, Venkata Samy Raja Jayagopalan, Venu Gopalakrishnan Sathyabama Inst Sci & Technol Dept ECE Chennai Tamil Nadu India Madha Engn Coll Dept ECE Chennai Tamil Nadu India

Now-a-days, the medical industry is growing a lot with the adaptation of latest technologies as well as the logical evaluation and security norms provides a robust platform to enhance the effectiveness of the industry at a drastic level. In this paper, a digital bio-medical image processing based Pneumonia disease identification system is introduced with enhanced security features. Due to improving the efficiency of the application, a well-known watermarking based security constraint is included to provide the protection to the respective hospital environment and patients as well. To avoid these issues, some sort of security aspects need to be followed so that this paper included watermarking based security to provide a rich level of protection to the images going to be tested. The main intention of this paper is to introduce a novel security enabled digital image processing scheme to identify the Pneumonic disease in earlier stages with respect to the proper classification principles. In this paper, a novel deep learning algorithm is introduced called enhanced Dynamic Learning Neural Network in which it is a hybrid algorithm with the combinations of conventional DLNN algorithm and the support vector classification algorithm. This proposed approach effectively identifies the Pneumonia disease in earlier stages but the security inspection on the testing stage is so important to analyze the disease. The respective testing image is properly watermarked with the logo of the corresponding hospital;the image is processed otherwise the proposed approach skips the image to process. These kinds of security features emphasize the medical industry and boost up the levels more as well as the patients can get an appropriate error free care with the help of such technology. A proper Chest X-Ray based Kaggle dataset is considered to process the system as well as which contains 5856 Chest X-Ray images under two different categories such as Pneumonia and Normal. With respect to proc

关键词： deep learning enhanced dynamic learning neural network support vector classification security pneumonia watermarking

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classification model selection via bilevel programming

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OPTIMIZATION METHODS & SOFTWARE 2008年第4期23卷 475-489页

作者： Kunapuli, G. Bennett, K. P. Hu, Jing Pang, Jong-Shi Rensselaer Polytech Inst Dept Math Sci Troy NY 12180 USA Univ Illinois Dept Ind & Enterprise Syst Engn Urbana IL 61801 USA

support vector machines and related classification models require the solution of convex optimization problems that have one or more regularization hyper-parameters. Typically, the hyper-parameters are selected to minimize the cross-validated estimates of the out-of-sample classification error of the model. This cross-validation optimization problem can be formulated as a bilevel program in which the outer-level objective minimizes the average number of misclassified points across the cross-validation folds, subject to inner-level constraints such that the classification functions for each fold are (exactly or nearly) optimal for the selected hyper-parameters. Feature selection is included in the bilevel program in the form of bound constraints in the weights. The resulting bilevel problem is converted to a mathematical program with linear equilibrium constraints, which is solved using state-of-the-art optimization methods. This approach is significantly more versatile than commonly used grid search procedures, enabling, in particular, the use of models with many hyper-parameters. Numerical results demonstrate the practicality of this approach for model selection in machine learning.

关键词： support vector classification cross-validation bilevel programming model selection feature selection

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On multi-site damage identification using single-site training data

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JOURNAL OF SOUND AND VIBRATION 2017年 409卷 43-64页

作者： Barthorpe, R. J. Manson, G. Worden, K. Univ Sheffield Dept Mech Engn Dynam Res Grp Mappin St Sheffield S1 3JD S Yorkshire England

This paper proposes a methodology for developing multi-site damage location systems for engineering structures that can be trained using single-site damaged state data only. The methodology involves training a sequence of binary classifiers based upon single-site damage data and combining the developed classifiers into a robust multi-class damage locator. In this way, the multi-site damage identification problem may be decomposed into a sequence of binary decisions. In this paper support vector Classifiers are adopted as the means of making these binary decisions. The proposed methodology represents an advancement on the state of the art in the field of multi-site damage identification which require either: (1) full damaged state data from single- and multi-site damage cases or (2) the development of a physics-based model to make multi-site model predictions. The potential benefit of the proposed methodology is that a significantly reduced number of recorded damage states may be required in order to train a multi-site damage locator without recourse to physics-based model predictions. In this paper it is first demonstrated that support vector classification represents an appropriate approach to the multi-site damage location problem, with methods for combining binary classifiers discussed. Next, the proposed methodology is demonstrated and evaluated through application to a real engineering structure - a Piper Tomahawk trainer aircraft wing - with its performance compared to classifiers trained using the full damaged-state dataset. (C) 2017 The Authors. Published by Elsevier Ltd.

关键词： Structural health monitoring support vector classification Multi-site damage identification Statistical pattern recognition

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First and Second Order SMO Algorithms for LS-SVM Classifiers

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NEURAL PROCESSING LETTERS 2011年第1期33卷 31-44页

作者： Lopez, Jorge Suykens, Johan A. K. Katholieke Univ Leuven ESAT SCD SISTA Dept Elect Engn B-3001 Leuven Belgium Univ Autonoma Madrid Dept Ingn Informat E-28049 Madrid Spain Univ Autonoma Madrid Inst Ingn Conocimiento E-28049 Madrid Spain

Least squares support vector machine (LS-SVM) classifiers have been traditionally trained with conjugate gradient algorithms. In this work, completing the study by Keerthi et al., we explore the applicability of the SMO algorithm for solving the LS-SVM problem, by comparing First Order and Second Order working set selections concentrating on the RBF kernel, which is the most usual choice in practice. It turns out that, considering all the range of possible values of the hyperparameters, Second Order working set selection is altogether more convenient than First Order. In any case, whichever the selection scheme is, the number of kernel operations performed by SMO appears to scale quadratically with the number of patterns. Moreover, asymptotic convergence to the optimum is proved and the rate of convergence is shown to be linear for both selections.

关键词： Least squares support vector machines Sequential minimal optimization support vector classification Working set selection

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Automated nuclear magnetic resonance fingerprinting of mixtures

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MAGNETIC RESONANCE IN CHEMISTRY 2024年第4期62卷 286-297页

作者： Specht, Thomas Arweiler, Justus Stueber, Johannes Muennemann, Kerstin Hasse, Hans Jirasek, Fabian RPTU Kaiserslautern Lab Engn Thermodynam LTD Kaiserslautern Germany RPTU Kaiserslautern Lab Engn Thermodynam LTD Erwin Schrodinger Str 44 D-67663 Kaiserslautern Germany

Nuclear magnetic resonance (NMR) spectroscopy is a powerful tool for qualitative and quantitative analysis. However, for complex mixtures, determining the speciation from NMR spectra can be tedious and sometimes even unfeasible. On the other hand, identifying and quantifying structural groups in a mixture from NMR spectra is much easier than doing the same for components. We call this group-based approach "NMR fingerprinting." In this work, we show that NMR fingerprinting can even be performed in an automated way, without expert knowledge, based only on standard NMR spectra, namely, C-13, H-1, and C-13 DEPT NMR spectra. Our approach is based on the machine-learning method of support vector classification (SVC), which was trained here on thousands of labeled pure-component NMR spectra from open-source data banks. We demonstrate the applicability of the automated NMR fingerprinting using test mixtures, of which spectra were taken using a simple benchtop NMR spectrometer. The results from the NMR fingerprinting agree remarkably well with the ground truth, which was known from the gravimetric preparation of the samples. To facilitate the application of the method, we provide an interactive website (), where spectral information can be uploaded and which returns the NMR fingerprint. The NMR fingerprinting can be used in many ways, for example, for process monitoring or thermodynamic modeling using group-contribution methods-or simply as a first step in species analysis.

关键词： C-13 H-1 benchtop NMR machine learning mixtures NMR SMARTS support vector classification

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Multiscale entropy-based approach to automated surface EMG classification of neuromuscular disorders

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MEDICAL & BIOLOGICAL ENGINEERING & COMPUTING 2010年第8期48卷 773-781页

作者： Istenic, Rok Kaplanis, Prodromos A. Pattichis, Constantinos S. Zazula, Damjan Univ Maribor Fac Elect Engn & Comp Sci SI-2000 Maribor Slovenia Cyprus Inst Neurol & Genet CY-1683 Nicosia Cyprus Univ Cyprus Dept Comp Sci CY-1678 Nicosia Cyprus

We introduce a novel method for an automatic classification of subjects to those with or without neuromuscular disorders. This method is based on multiscale entropy of recorded surface electromyograms (sEMGs) and support vector classification. The method was evaluated on a single-channel experimental sEMGs recorded from biceps brachii muscle of nine healthy subjects, nine subjects with muscular and nine subjects with neuronal disorders, at 10%, 30%, 50%, 70% and 100% of maximal voluntary contraction force. Leave-one-out cross-validation was performed, deploying binary (healthy/patient) and three-class classification (healthy/myopathic/neuropathic). In the case of binary classification, subjects were distinguished with 81.5% accuracy (77.8% sensitivity at 83.3% specificity). At three-class classification, the accuracy decreased to 70.4% (myopathies were recognized with a sensitivity of 55.6% at specificity 88.9%, neuropathies with a sensitivity of 66.7% at specificity 83.3%). The proposed method is suitable for fast and non-invasive discrimination of healthy and neuromuscular patient groups, but it fails to recognize the type of pathology.

关键词： Surface electromyogram Neuromuscular disorders Continuous wavelet transform Entropy support vector classification

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Prediction of Antibacterial Compounds by Machine Learning Approaches

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JOURNAL OF COMPUTATIONAL CHEMISTRY 2009年第8期30卷 1202-1211页

作者： Yang, Xue-Gang Chen, Duan Wang, Min Xue, Ying Chen, Yu-Zong Sichuan Univ Coll Chem Minist Educ Key Lab Green Chem & Technol Chengdu 610064 Peoples R China Sichuan Univ State Key Lab Biotherapy Chengdu 610041 Peoples R China Natl Univ Singapore Dept Computat Sci Bioinformat & Drug Design Grp Singapore 117543 Singapore

The machine learning (ML) as well as quantitative structure activity relationship (QSAR) method has been explored for predicting compounds with antibacterial activities at impressive performance. It is desirable to test additional ML methods, select most representative sets of molecular descriptors, and subject the developed prediction models to rigorous evaluations. This work evaluated three ML methods, support vector classification (SVC), k-nearest neighbor (k-NN), and C4.5 decision tree, which were trained and tested by 230 antibacterial and 381 nonantibacterial compounds. A well-established feature selection method was used to select representative molecular descriptors from a larger pool than that used in reported studies. The performance of the developed prediction models was tested by 5-fold cross-validation and independent evaluation set. SVC produced the best prediction accuracies of 96.66 and 98.15% for antibacterial compounds, and 99.50 and 98.02% for nonantibacterial compounds respectively, which are slightly improved against those of the reported ML as well as QSAR models and outperform the k-NN and C4.5 decision tree models developed in this work. Our study suggests that ML methods, particularly SVC, are potentially useful for facilitating the discovery of antibacterial agents. (C) 2008 Wiley Periodicals, Inc. J Comput Chem 30: 1202-1211, 2009

关键词： Antibacterial activities of compounds support vector classification k-nearest neighbor C4.5 decision tree

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Recognition of candidate aptamer sequences for human hepatocellular carcinoma in SELEX screening using structure-activity relationships

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CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS 2014年 136卷 10-14页

作者： Yu, Xinliang Yu, Ruqin Tang, Lijuan Guo, Qiuping Zhang, Yan Zhou, Yu Yang, Qin He, Xiaoxiao Yang, Xiaohai Wang, Kemin Hunan Univ Coll Chem & Chem Engn State Key Lab Chemo Biosensing & Chemometr Changsha 410082 Hunan Peoples R China Hunan Inst Engn Coll Chem & Chem Engn Xiangtan 411104 Hunan Peoples R China Hunan Univ Coll Biol Changsha 410082 Hunan Peoples R China

Selecting and synthesizing aptamers for human hepatic carcinoma (HCC) cells with high affinity and specificity would be of critical importance for diagnosing liver cancer early. This paper is the first report on pattern recognition used for SELEX-based aptamer screening by applying a support vector classification (SVC) technique for a two-class problem. The candidate aptamer sequences that show different degrees of affinity and specificity for SMMC-7721 liver carcinoma cells were selected through whole cell-SELEX. After calculating 1670 molecular descriptors, 13 descriptors were selected, which were compressed to 6 latent variables used as the inputs for classification models. The predicted fractions of winner aptamers from the SELEX selection of the 3rd, 5th, 7th, 9th, 11th, and 13th rounds are 0.033, 0.427, 0.678, 0.828, 0.912 and 0.983, respectively, which conform to the aptamer evolutionary principles of SELEX based screening. By the pattern recognition analysis based on a structure-activity relationship (SAR) model, 6 DNA candidate aptamer sequences belonging to the class of sequences with high affinity and specificity have experimental dissociation constants K-d in the nanomolar range. The feasibility of applying pattern recognition for the design and selection of aptamers has been demonstrated. (C) 2014 Elsevier B.V. All rights reserved.

关键词： Aptamer Human hepatocellular carcinoma Pattern recognition Structure-activity relationship SELEX support vector classification

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A second-order cone programming formulation for twin support vector machines

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APPLIED INTELLIGENCE 2016年第2期45卷 265-276页

作者： Maldonado, Sebastian Lopez, Julio Carrasco, Miguel Univ Los Andes Fac Ingn & Ciencias Aplicadas Santiago 12455 Chile Univ Diego Portales Fac Ingn Ejercito 441 Santiago Chile

Second-order cone programming (SOCP) formulations have received increasing attention as robust optimization schemes for support vector Machine (SVM) classification. These formulations study the worst-case setting for class-conditional densities, leading to potentially more effective classifiers in terms of performance compared to the standard SVM formulation. In this work we propose an SOCP extension for Twin SVM, a recently developed classification approach that constructs two nonparallel classifiers. The linear and kernel-based SOCP formulations for Twin SVM are derived, while the duality analysis provides interesting geometrical properties of the proposed method. Experiments on benchmark datasets demonstrate the virtues of our approach in terms of classification performance compared to alternative SVM methods.

关键词： support vector classification Twin support vector machines Second-order cone programming

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