基于支持向量机的步态分类方法

针对小样本步态数据引起的分类器泛化能力差的问题,提出了基于支持向量机的步态分类方法.采集了24名青年和24名老年受试者的步态数据,提取24个步态特征训练支持向量机,采用交叉验证方法评估分类器的泛化性能.结果表明,本文提出的方法能够有效地对小样本步态数据分类,并且具有良好的泛化性.不同的核函数对分类性能影响较小.与传统反向传播学习算法的神经网络分类器进行了比较,支持向量机分类性能明显优于传统反向传播学习算法的神经网络.支持向量机在步态分类中具有广泛的应用前景.     

Multi-class differentiation of cannabis seedlings in a forensic context

This article presents an experimental study about the classification ability of several classifiers for multi-class classification of cannabis seedlings. As the cultivation of drug type cannabis is forbidden in Switzerland law enforcement authorities regularly ask forensic laboratories to determinate the chemotype of a seized cannabis plant and then to conclude if the plantation is legal or not. This classification is mainly performed when the plant is mature as required by the EU official protocol and then the classification of cannabis seedlings is a time consuming and costly procedure. A previous study made by the authors has investigated this problematic [1] and showed that it is possible to differentiate between drug type (illegal) and fibre type (legal) cannabis at an early stage of growth using gas chromatography interfaced with mass spectrometry (GC-MS) based on the relative proportions of eight major leaf compounds. The aims of the present work are on one hand to continue former work and to optimize the methodology for the discrimination of drug- and fibre type cannabis developed in the previous study and on the other hand to investigate the possibility to predict illegal cannabis varieties. Seven classifiers for differentiating between cannabis seedlings are evaluated in this paper, namely Linear Discriminant Analysis (LDA), Partial Least Squares Discriminant Analysis (PLS-DA), Nearest Neighbour Classification (NNC), Learning Vector Quantization (LVQ), Radial Basis Function Support Vector Machines (RBF SVMs), Random Forest (RF) and Artificial Neural Networks (ANN). The performance of each method was assessed using the same analytical dataset that consists of 861 samples split into drug- and fibre type cannabis with drug type cannabis being made up of 12 varieties (i.e. 12 classes). The results show that linear classifiers are not able to manage the distribution of classes in which some overlap areas exist for both classification problems. Unlike linear classifiers, NNC and RBF SVMs best differentiate cannabis samples both for 2-class and 12-class classifications with average classification results up to 99% and 98%, respectively. Furthermore, RBF SVMs correctly classified into drug type cannabis the independent validation set, which consists of cannabis plants coming from police seizures. In forensic case work this study shows that the discrimination between cannabis samples at an early stage of growth is possible with fairly high classification performance for discriminating between cannabis chemotypes or between drug type cannabis varieties.     

A particle swarm‐optimized support vector machine for reliability prediction

System reliability depends on inherent mechanical and structural aging factors as well as on operational and environmental conditions, which could enhance (or smoothen) such factors. In practice, the involved dependences may burden the modeling of the reliability behavior over time, in which traditional stochastic modeling approaches may likely fail. Empirical prediction methods, such as support vector machines (SVMs), become a valid alternative whenever reliable time series data are available. However, the prediction performance of SVMs depends on the setting of a number of parameters that influence the effectiveness of the training stage during which the SVMs are constructed based on the available data set. The problem of choosing the most suitable values for the SVM parameters can be framed in terms of an optimization problem aimed at minimizing a prediction error. In this work, this problem is solved by particle swarm optimization (PSO), a probabilistic approach based on an analogy with the collective motion of biological organisms. SVM in liaison with PSO is then applied to tackle reliability prediction problems based on time series data of engineered components. Comparisons of the obtained results with those given by other time series techniques indicate that the PSO + SVM model is able to provide reliability predictions with comparable or great accuracy. Copyright © 2011 John Wiley & Sons, Ltd.     

Transmission line distance relaying using machine intelligence technique

A new approach for distance relaying of transmission line using machine intelligence technique such as support vector machine (SVM) is presented. The proposed SVM technique is used for faulty phase selection and ground detection in different fault situations that occur on large power transmission line. Post-fault current and voltage samples for one-fourth cycle (five samples) are used as inputs to SVM 1, which provide output for faulty phase selection. SVM 2 is trained and tested with zero-sequence components of fundamental, third and fifth harmonic components of the post-fault current signal to provides the involvement of ground in the fault process. The polynomial and Gaussian kernel SVMs are designed to provide the most optimised boundary for classification. The total time taken for faulty phase selection and ground detection is 10 ms (half cycle) from the inception of fault. Also the proposed technique is tested on experimental set-up with different fault situations. The test results are compared with those of the radial basis function neural network and were found to be superior with respect to efficiency and speed. The classification test results from SVMs are accurate for simulation model and experimental set-up, and thus provide fast and robust protection scheme for distance relaying in transmission line.     

Data-mining-based dynamic dispatching rule selection mechanism for shop floor control systems using a support vector machine approach

The purpose of this paper is to develop a data-mining-based dynamic dispatching rule selection mechanism for a shop floor control system to make real-time scheduling decisions. In data mining processes, data transformations (including data normalisation and feature selection) and data mining algorithms greatly influence the predictive accuracy of data mining tasks. Here, the z-scores data normalisation mechanism and genetic-algorithm-based feature selection mechanism are used for data transformation tasks, then support vector machines (SVMs) is applied for the dynamic dispatching rule selection classifier. The simulation experiments demonstrate that the proposed data-mining-based approach is more generalisable than approaches that do not employ a data-mining-based approach, in terms of accurately assigning the best dispatching strategy for the next scheduling period. Moreover, the proposed SVM classifier using the data-mining-based approach yields a better system performance than obtained with a classical SVM-based dynamic dispatching rule selection mechanism and heuristic individual dispatching rules under various performance criteria over a long period.     

Analysis and classification of malignancy in pancreatic magnetic resonance images using neural network techniques

Computer-aided diagnosis (CAD) is a computerized way of detecting tumors in MR images. Magnetic resonance imaging (MRI) has been generally used in the diagnosis and detection of pancreatic tumors. In a medical imaging system, soft tissue contrast and noninvasiveness are clear preferences of MRI. Inaccurate detection of tumor and long time consumption are the disadvantages of MRI. Computerized classifiers can greatly renew the diagnosis activity, in terms of both accuracy and time necessity by normal and abnormal images, automatically. This article presents an intelligent, automatic, accurate, and robust method to classify human pancreas MRI images as normal or abnormal in terms of pancreatic tumor. It represents the response of artificial neural network (ANN) and support vector machine (SVM) techniques for pancreatic tumor classification. For this, we extract features from MR images of pancreas using the GLCM method and select the best features using JAFER algorithm. These features are analyzed by five classification techniques: ANN BP, ANN RBF, SVM Linear, SVM Poly, and SVM RBF. We compare the results with benchmark data set of MR brain images. The analytical outcome presents that the two best features used to classify the MR images using ANN BP technique have 98% classification accuracy.     

改进MDSMOTE与PSO-SVM在汽车组合仪表分类预测中的应用

汽车组合仪表生产过程中质检项目多且检测时间长,这在一定程度上制约了其生产效率的进一步提升。为此,提出一种基于改进最远点合成少数类过采样技术（max distance synthetic minority over-sampling technique,MDSMOTE）的支持向量机（support vector machine, SVM）分类预测方法。首先,结合专家经验对汽车组合仪表的原始生产数据进行特征筛选,并在MDSMOTE中引入类不平衡率I_R,以对所筛选的特征数据进行扩充;然后,利用粒子群优化（particle swarm optimization, PSO）算法对SVM的误差惩罚因子C和核函数参数γ进行优化;最后,建立优化的SVM分类预测模型,并对汽车组合仪表进行分类。通过与其他分类预测模型在不同数据集上的预测结果进行对比可知,基于改进MDSMOTE的SVM分类预测模型的准确率、F值和几何平均值等评价指标均优于其他模型。所提出方法在汽车仪表产品分类上表现出较强的泛化能力和稳定性,可为仪表制造企业生产效率的提升提供有效参考。     

Obscure bleeding detection in endoscopy images using support vector machines

Wireless capsule endoscopy (WCE) is a recently established imaging technology that requires no wired device intrusion and can be used to examine the entire small intestine non-invasively. Determining bleeding signs out of over 55,000 WCE images is a tedious and expensive job by human reviewing. Our goal is to develop an automatic obscure bleeding detection method by employing image color features and support vector machine (SVM) classifier. The bleeding lesion detection problem is a binary classification problem. We use SVMs for this problem and a new feature selection procedure is proposed. Our experiments show that SVM can be very efficient in processing unseen instances and may yield very high accuracy rate, in particular with our new proposed feature selection. More specifically, for this bleeding detection problem, training an SVM with 640 samples can be completed in as little as 0.01  second on a Dell workstation with dual Xeon CPUs, and classifying an image using the trained SVM can be done in as little as 0.03 milliseconds. The accuracy for both sensitivity and specificity can be over 99%. This work was partially supported by National Science Foundation grant IIS-0722106, IIS-0737861, and Texas ARP 003594-0020-2007.     

Failure and reliability prediction by support vector machines regression of time series data

Support Vector Machines (SVMs) are kernel-based learning methods, which have been successfully adopted for regression problems. However, their use in reliability applications has not been widely explored. In this paper, a comparative analysis is presented in order to evaluate the SVM effectiveness in forecasting time-to-failure and reliability of engineered components based on time series data. The performance on literature case studies of SVM regression is measured against other advanced learning methods such as the Radial Basis Function, the traditional MultiLayer Perceptron model, Box-Jenkins autoregressive-integrated-moving average and the Infinite Impulse Response Locally Recurrent Neural Networks. The comparison shows that in the analyzed cases, SVM outperforms or is comparable to other techniques.     

Artificial neural networks as alternative tool for minimizing error predictions in manufacturing ultradeformable nanoliposome formulations

This work was aimed at determining the feasibility of artificial neural networks (ANN) by implementing backpropagation algorithms with default settings to generate better predictive models than multiple linear regression (MLR) analysis. The study was hypothesized on timolol-loaded liposomes. As tutorial data for ANN, causal factors were used, which were fed into the computer program. The number of training cycles has been identified in order to optimize the performance of the ANN. The optimization was performed by minimizing the error between the predicted and real response values in the training step. The results showed that training was stopped at 10?000 training cycles with 80% of the pattern values, because at this point the ANN generalizes better. Minimum validation error was achieved at 12 hidden neurons in a single layer. MLR has great prediction ability, with errors between predicted and real values lower than 1% in some of the parameters evaluated. Thus, the performance of this model was compared to that of the MLR using a factorial design. Optimal formulations were identified by minimizing the distance among measured and theoretical parameters, by estimating the prediction errors. Results indicate that the ANN shows much better predictive ability than the MLR model. These findings demonstrate the increased efficiency of the combination of ANN and design of experiments, compared to the conventional MLR modeling techniques.     

One‐sided control chart based on support vector machines with differential evolution algorithm

The statistical learning classification techniques have been successfully applied to statistical process control problems. In this paper, we proposed a one‐sided control chart based on support vector machines (SVMs) and differential evolution (DE) algorithm to monitor a process with multivariate quality characteristics. The SVM classifier provides a continuous distance from the boundary, and the DE algorithm is used to obtain the optimal parameters of the SVM model by minimizing mean absolute error (MAE). The average run length of the proposed chart is computed using the Monte Carlo simulation approach. Several simulated cases are conducted using a multivariate normal distribution with 10 and 20 dimensions and three different process shift scenarios. In addition, we consider two non‐normal distribution cases. The ARL performance of the proposed chart is better than the distance‐based SVM chart. A real example is used to illustrate the application of the proposed control chart.     

Learning methods for melanoma recognition

Melanoma is the most deadly skin cancer. Early diagnosis is a challenge for clinicians. Current algorithms for skin lesions' classification focus mostly on segmentation and feature extraction. This article instead puts the emphasis on the learning process, testing the recognition performance of three different classifiers: support vector machine (SVM), artificial neural network and k‐nearest neighbor. Extensive experiments were run on a database of more than 5000 dermoscopy images. The obtained results show that the SVM approach outperforms the other methods reaching an average recognition rate of 82.5% comparable with those obtained by skilled clinicians. If confirmed, our data suggest that this method may improve classification results of a computer‐assisted diagnosis of melanoma. © 2010 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 20, 316–322, 2010     

Detection of the Pine Wilt Disease Tree Candidates for Drone Remote Sensing Using Artificial Intelligence Techniques

Pine wilt disease (PWD) has recently caused substantial pine tree losses in Republic of Korea. PWD is considered a severe problem due to the importance of pine trees to Korean people, so this problem must be handled appropriately. Previously, we examined the history of PWD and found that it had already spread to some regions of Republic of Korea; these became our study area. Early detection of PWD is required. We used drone remote sensing techniques to detect trees with similar symptoms to trees infected with PWD. Drone remote sensing was employed because it yields high-quality images and can easily reach the locations of pine trees. To differentiate healthy pine trees from those with PWD, we produced a land cover (LC) map from drone images collected from the villages of Anbi and Wonchang by classifying them using two classifier methods, i.e., artificial neural network (ANN) and support vector machine (SVM). Furthermore, compared the accuracy of two types of Global Positioning System (GPS) data, collected using drone and hand-held devices, for identifying the locations of trees with PWD. We then divided the drone images into six LC classes for each study area and found that the SVM was more accurate than the ANN at classifying trees with PWD. In Anbi, the SVM had an overall accuracy of 94.13%, which is 6.7% higher than the overall accuracy of the ANN, which was 87.43%. We obtained similar results in Wonchang, for which the accuracy of the SVM and ANN was 86.59% and 79.33%, respectively. In terms of the GPS data, we used two type of hand-held GPS device. GPS device 1 is corrected by referring to the benchmarks sited on both locations, while the GPS device 2 is uncorrected device which used the default setting of the GPS only. The data collected from hand-held GPS device 1 was better than those collected using hand-held GPS device 2 in Wonchang. However, in Anbi, we obtained better results from GPS device 2 than from GPS device 1. In Anbi, the error in the data from GPS device 1 was 7.08 m, while that of the GPS device 2 data was 0.14 m. In conclusion, both classifiers can distinguish between healthy trees and those with PWD based on LC data. LC data can also be used for other types of classification. There were some differences between the hand-held and drone GPS datasets from both areas.     

Support Vector Machine Applications in Terahertz Pulsed Signals Feature Sets

In the past decade, terahertz radiation (T-rays) have been extensively applied within the fields of industrial and biomedical imaging, owing to their noninvasive property. Support vector machine (SVM) learning algorithms are sufficiently powerful to detect patterns hidden inside noisy biomedical measurements. This paper introduces a frequency orientation component method to extract T-ray feature sets for the application of two- and multiclass classification using SVMs. Effective discriminations of ribonucleic acid (RNA) samples and various powdered substances are demonstrated. The development of this method has become important in T-ray chemical sensing and image processing, which results in enhanced detectability useful for many applications, such as quality control, security detection and clinic diagnosis.     

Effective Classification of Synovial Sarcoma Cancer Using Structure Features and Support Vectors

In this research work, we proposed a medical image analysis framework with two separate releases whether or not Synovial Sarcoma (SS) is the cell structure for cancer. Within this framework the histopathology images are decomposed into a third-level sub-band using a two-dimensional Discrete Wavelet Transform. Subsequently, the structure features (SFs) such as Principal Components Analysis (PCA), Independent Components Analysis (ICA) and Linear Discriminant Analysis (LDA) were extracted from this sub-band image representation with the distribution of wavelet coefficients. These SFs are used as inputs of the Support Vector Machine (SVM) classifier. Also, classification of PCA + SVM, ICA + SVM, and LDA + SVM with Radial Basis Function (RBF) kernel the efficiency of the process is differentiated and compared with the best classification results. Furthermore, data collected on the internet from various histopathological centres via the Internet of Things (IoT) are stored and shared on blockchain technology across a wide range of image distribution across secure data IoT devices. Due to this, the minimum and maximum values of the kernel parameter are adjusted and updated periodically for the purpose of industrial application in device calibration. Consequently, these resolutions are presented with an excellent example of a technique for training and testing the cancer cell structure prognosis methods in spindle shaped cell (SSC) histopathological imaging databases. The performance characteristics of cross-validation are evaluated with the help of the receiver operating characteristics (ROC) curve, and significant differences in classification performance between the techniques are analyzed. The combination of LDA + SVM technique has been proven to be essential for intelligent SS cancer detection in the future, and it offers excellent classification accuracy, sensitivity, specificity.     

基于支持向量机的小样本响应曲面法研究

当影响因素和响应输出的关系较为复杂时,应用传统响应曲面法(RSM)、非参数响应曲面法(NPRSM)和人工神经网络(ANN)难以拟合真实的响应曲面,不仅需要大的样本量,而且泛化风险大,不易达到全局最优.将RSM归结为可有限制地主动获取样本的小样本机器学习问题,提出了一种基于支持向量机(SVM)的RSM.以大间隔网格取样,利用SVM拟合过程,对拟合方程寻优确定极值大致区域,再逐步缩小间隔求精.算例研究表明,该方法的拟合与泛化性能优于NPRSM和基于ANN的RSM,能在小样本条件下建立全局性数值模型,寻优可以得到多个极值.     

Providing an efficient intelligent transportation system through detection,tracking and recognition of the region of interest in traffic signs by using non-linear SVM classifier in line with histogram oriented gradient and Kalman filter approach

With a focus on new researches in the area of intelligent transportation systems (ITS), an efficient approach has been investigated here. Based on the present view point, analysis of traffic signs are first considered via intelligence based approach, which is carried out through three main stages including detection, tracking and recognition, respectively, in this research. The key role of detection is to identify traffic signs by classification of road sign shapes in accordance with their signatures. This classification consists of four different shapes of circle, semicircle, triangle and square, as well. The linear classification of traffic sign is also carried out via support vector machine (SVM) by using one against all (OAA), since the present SVMs classifiers realized via linear kernel. The next step is to track traffic sign. It should be noted that this technique is now developed to reduce the searching mode in case of the whole area to be optimized its computational processing, consequently. This research work is investigated by realizing Kalman filter approach, where, finally, in recognition step, a feature of the region of interest (ROI) has been extracted for SVM classification. Histogram of oriented gradient (HOG) is realized in organizing the approach, as long as Gaussian kernel is also developed for non-linear SVM classifier.     

Application research on semi-definite programming optimized support vector machines

The kernel function optimization is the key issues to address when using the support vector machine (SVM) algorithm. To solve the parameter selection for the SVM, a semi-definite programming optimized SVM (SDP-SVM) algorithm is proposed in this paper. The steps of the algorithm are described, and the optimization of the kernel function is shown using an SDP method. The SDP method is used to find the best parameter of SVM. The heart_scale data in the University of California Irvine database are then simulated using the SDP-SVM model. The experimental results shows that the generalization capability and the classification accuracy of the SDP-SVM algorithm have been greatly improved. A variety of strip-steel surface defect images from actual production are classified using the SDP-SVM algorithm, and the results show that the classification method of the SDP-SVM algorithm has high classification accuracy, strong practicability, and a wide variety of application prospects.     

Rule generation for protein secondary structure prediction with support vector machines and decision tree

Support vector machines (SVMs) have shown strong generalization ability in a number of application areas, including protein structure prediction. However, the poor comprehensibility hinders the success of the SVM for protein structure prediction. The explanation of how a decision made is important for accepting the machine learning technology, especially for applications such as bioinformatics. The reasonable interpretation is not only useful to guide the "wet experiments," but also the extracted rules are helpful to integrate computational intelligence with symbolic AI systems for advanced deduction. On the other hand, a decision tree has good comprehensibility. In this paper, a novel approach to rule generation for protein secondary structure prediction by integrating merits of both the SVM and decision tree is presented. This approach combines the SVM with decision tree into a new algorithm called SVM/spl I.bar/DT, which proceeds in three steps. This algorithm first trains an SVM. Then, a new training set is generated through careful selection from the output of the SVM. Finally, the obtained training set is used to train a decision tree learning system and to extract the corresponding rule sets. The results of the experiments of protein secondary structure prediction on RS126 data set show that the comprehensibility of SVM/spl I.bar/DT is much better than that of the SVM. Moreover, the generalization ability of SVM/spl I.bar/DT is better than that of C4.5 decision trees and is similar to that of the SVM. Hence, SVM/spl I.bar/DT can be used not only for prediction, but also for guiding biological experiments.