SVM分类核函数及参数选择比较

支持向量机（SVM）被证实在分类领域性能良好,但其分类性能受到核函数及参数影响。讨论核函数及参数对SVM分类性能的影响,并运用交叉验证与网格搜索法进行参数优化选择,为SVM分类核函数及参数选择提供借鉴。     

Support Vector Machines for TCP traffic classification

Support Vector Machines (SVM) represent one of the most promising Machine Learning (ML) tools that can be applied to the problem of traffic classification in IP networks. In the case of SVMs, there are still open questions that need to be addressed before they can be generally applied to traffic classifiers. Having being designed essentially as techniques for binary classification, their generalization to multi-class problems is still under research. Furthermore, their performance is highly susceptible to the correct optimization of their working parameters. In this paper we describe an approach to traffic classification based on SVM. We apply one of the approaches to solving multi-class problems with SVMs to the task of statistical traffic classification, and describe a simple optimization algorithm that allows the classifier to perform correctly with as little training as a few hundred samples. The accuracy of the proposed classifier is then evaluated over three sets of traffic traces, coming from different topological points in the Internet. Although the results are relatively preliminary, they confirm that SVM-based classifiers can be very effective at discriminating traffic generated by different applications, even with reduced training set sizes.     

Twin Support Vector Machines for pattern classification

We propose twin SVM, a binary SVM classifier that determines two nonparallel planes by solving two related SVM-type problems, each of which is smaller than in a conventional SVM. The twin SVM formulation is in the spirit of proximal SVMs via generalized eigenvalues. On several benchmark data sets, Twin SVM is not only fast, but shows good generalization. Twin SVM is also useful for automatically discovering two-dimensional projections of the data     

Engine ignition signal diagnosis with Wavelet Packet Transform and Multi-class Least Squares Support Vector Machines

Engine ignition pattern analysis is one of the trouble-diagnosis methods for automotive gasoline engines. Based on the waveform of the ignition pattern, the mechanic guesses what may be the potential malfunctioning parts of an engine with his/her experience and handbooks. However, this manual diagnostic method is imprecise because many ignition patterns are very similar. Therefore, a diagnosis may need many trials to identify the malfunctioning parts. Meanwhile the mechanic needs to disassemble and assemble the engine parts for verification. To tackle this problem, Wavelet Packet Transform (WPT) is firstly employed to extract the features of the ignition pattern. With the extracted features, a statistics over the frequency subbands of the pattern can then be produced, which can be used by Multi-class Least Squares Support Vector Machines (MCLS-SVM) for engine fault classification. With the newly proposed classification system, the number of diagnostic trials can be reduced. Besides, MCLS-SVM is also compared with a typical classification method, Multi-layer Perceptron (MLP). Experimental results show that MCLS-SVM produces higher diagnostic accuracy than MLP.     

一种自动选择参数的加权支持向量机算法

C-SVM分类算法在不同类别样本数目不均衡的情况下,训练时的分类错误倾向于样本数目小的类别。样本集中出现重复样本时作为新样本重新计算,增加了算法的训练时间。针对这两种问题,分析了产生的原因,提出了一种加权支持向量机算法,补偿了类别差异造成的不利影响,加快了重复样本的决策速度。为提高算法的推广性能,在模型训练过程中引入遗传算法自动选择惩罚因子和核函数宽度两个参数。实验结果表明了该算法可以有效地解决类别不均衡和重复样本问题,且训练模型具有良好的推广性能。     

基于记忆的SVM相关反馈算法

支持向量机(SVM)方法并不假设样本的分布条件,它基于结构风险最小化原则,对小样本情况下的学习问 题给出最优解,并且在样本趋于无穷时能保持良好的一致收敛性。在SVM的基础上提出的MSVM方法,通过记忆 功能,用历次反馈的累积样本代替一次反馈样本,从而增加了学习样本数量,减小了查准率的振荡,提高了检索精度; 同时为了减轻用户负担,提出了记忆性标注。实验证明,MSVM方法可以避免因训练样本集过小而出现的局部最小 化的问题,能较为准确地分类图像库中的图像,同时有效地减轻了用户的负担。     

基于记忆的SVM相关反馈算法

支持向量机(SVM)方法并不假设样本的分布条件,它基于结构风险最小化原则,对小样本情况下的学习问题给出最优解,并且在样本趋于无穷时能保持良好的一致收敛性.在SVM的基础上提出的MSVM方法,通过记忆功能,用历次反馈的累积样本代替一次反馈样本,从而增加了学习样本数量,减小了查准率的振荡,提高了检索精度;同时为了减轻用户负担,提出了记忆性标注.实验证明,MSVM方法可以避免因训练样本集过小而出现的局部最小化的问题,能较为准确地分类图像库中的图像,同时有效地减轻了用户的负担.     

Complex networks approach for EEG signal sleep stages classification

Sleep stage scoring is a challenging task. Most of existing sleep stage classification approaches rely on analysing electroencephalography (EEG) signals in time or frequency domain. A novel technique for EEG sleep stages classification is proposed in this paper. The statistical features and the similarities of complex networks are used to classify single channel EEG signals into six sleep stages. Firstly, each EEG segment of 30 s is divided into 75 sub-segments, and then different statistical features are extracted from each sub-segment. In this paper, feature extraction is important to reduce dimensionality of EEG data and the processing time in classification stage. Secondly, each vector of the extracted features, which represents one EEG segment, is transferred into a complex network. Thirdly, the similarity properties of the complex networks are extracted and classified into one of the six sleep stages using a k-means classifier. For further investigation, in the statistical features extraction phase two statistical features sets are tested and ranked based on the performance of the complex networks. To investigate the classification ability of complex networks combined with k-means, the extracted statistical features were also forwarded to a k-means and a support vector machine (SVM) for comparison. We also compare the proposed method with other existing methods in the literature. The experimental results show that the proposed method attains better classification results and a reasonable execution time compared with the SVM, k-means and the other existing methods. The research results in this paper indicate that the proposed method can assist neurologists and sleep specialists in diagnosing and monitoring sleep disorders.     

The random electrode selection ensemble for EEG signal classification

Pattern classification methods are a crucial direction in the current study of brain–computer interface (BCI) technology. A simple yet effective ensemble approach for electroencephalogram (EEG) signal classification named the random electrode selection ensemble (RESE) is developed, which aims to surmount the instability demerit of the Fisher discriminant feature extraction for BCI applications. Through the random selection of recording electrodes answering for the physiological background of user-intended mental activities, multiple individual classifiers are constructed. In a feature subspace determined by a couple of randomly selected electrodes, principal component analysis (PCA) is first used to carry out dimensionality reduction. Successively Fisher discriminant is adopted for feature extraction, and a Bayesian classifier with a Gaussian mixture model (GMM) approximating the feature distribution is trained. For a test sample the outputs from all the Bayesian classifiers are combined to give the final prediction for its label. Theoretical analysis and classification experiments with real EEG signals indicate that the RESE approach is both effective and efficient.     

Support Vector Machines for classification and locating faults on transmission lines

This paper presents a new approach to classify fault types and predict the fault location in the high-voltage power transmission lines, by using Support Vector Machines (SVM) and Wavelet Transform (WT) of the measured one-terminal voltage and current transient signals. Wavelet entropy criterion is applied to wavelet detail coefficients to reduce the size of feature vector before classification and prediction stages. The experiments performed for different kinds of faults occurred on the transmission line have proved very good accuracy of the proposed fault location algorithm. The fault classification error is below 1% for all tested fault conditions. The average error of fault location in a 380 kV–360-km transmission line is below 0.26% and the maximum error did not exceed 0.95 km.     

支持向量机在脑电信号分类中的应用

首先采用小波变换提取精神分裂症与健康人的脑电信号频率和空间的能量特征,然后用基于统计学习理论的支持向量机(SVM)分类器进行训练和分类测试,并比较了不同核函数和参数对脑电信号分类正确率的影响,最后与RBF神经网络的分类能力进行了实验比较。试验结果表明,利用基于支持向量机和能量特征的方法实现对脑电信号的分类可以取得理想的效果,精神分裂症患者和健康人的16导脑电信号在能量特征上表现出较高的模式可分性。这种分类方法在精神分裂症患者的病理诊断中具有一定的应用价值。     

Autoencoder-based improved deep learning approach for schizophrenic EEG signal classification

Pattern Analysis and Applications - In this paper, deep-stacked error minimized extreme learning machine autoencoder (DSEMELMAE) and sine–cosine monarch butterfly optimization-based minimum...     

A comparative study of wavelet families for EEG signal classification

Over the past two decades, wavelet theory has been used for the processing of biomedical signals for feature extraction, compression and de-noising applications. However the question as to which wavelet family is the most suitable for analysis of non-stationary bio-signals is still prevalent among researchers. This paper attempts to find the most useful wavelet function among the existing members of the wavelet families for electroencephalogram signal (EEG) analysis. The EEGs considered for this study belong to both normal as well as abnormal signals like epileptic EEG. Important features such as energy, entropy and standard deviation at different sub-bands were computed using the wavelet functions—Haar, Daubechies (orders 2-10), Coiflets (orders 1-10), and Biorthogonal (orders 1.1, 2.4, 3.5, and 4.4). Feature vectors were used to model and train the Probabilistic Neural Network (PNN) and the classification accuracies were evaluated for each case. The results obtained from PNN classifier were compared with Support Vector Machine (SVM) classifier. From the statistical analysis, it was found that Coiflets 1 is the most suitable candidate among the wavelet families considered in this study for accurate classification of the EEG signals. In this work, we have attempted to improve the computing efficiency as it selects the most suitable wavelet function that can be used for EEG signal processing efficiently and accurately with lesser computational time.     

Drug/nondrug classification using Support Vector Machines with various feature selection strategies

In conjunction with the advance in computer technology, virtual screening of small molecules has been started to use in drug discovery. Since there are thousands of compounds in early-phase of drug discovery, a fast classification method, which can distinguish between active and inactive molecules, can be used for screening large compound collections. In this study, we used Support Vector Machines (SVM) for this type of classification task. SVM is a powerful classification tool that is becoming increasingly popular in various machine-learning applications. The data sets consist of 631 compounds for training set and 216 compounds for a separate test set. In data pre-processing step, the Pearson's correlation coefficient used as a filter to eliminate redundant features. After application of the correlation filter, a single SVM has been applied to this reduced data set. Moreover, we have investigated the performance of SVM with different feature selection strategies, including SVM–Recursive Feature Elimination, Wrapper Method and Subset Selection. All feature selection methods generally represent better performance than a single SVM while Subset Selection outperforms other feature selection methods. We have tested SVM as a classification tool in a real-life drug discovery problem and our results revealed that it could be a useful method for classification task in early-phase of drug discovery.     

Handling binary classification problems with a priority class by using Support Vector Machines

A post-processing technique for Support Vector Machine (SVM) algorithms for binary classification problems is introduced in order to obtain adequate accuracy on a priority class (labelled as a positive class). That is, the true positive rate (or recall or sensitivity) is prioritized over the accuracy of the overall classifier. Hence, false negative (or Type I) errors receive greater consideration than false positive (Type II) errors during the construction of the model.This post-processing technique tunes the initial bias term once a solution vector is learned by using standard SVM algorithms in two steps: First, a fixed threshold is given as a lower bound for the recall measure; second, the true negative rate (or specificity) is maximized.Experiments, carried out on eleven standard UCI datasets, show that the modified SVM satisfies the aims for which it has been designed. Furthermore, results are comparable or better than those obtained when other state-of-the-art SVM algorithms and other usual metrics are considered.     

Clustering technique-based least square support vector machine for EEG signal classification

This paper presents a new approach called clustering technique-based least square support vector machine (CT-LS-SVM) for the classification of EEG signals. Decision making is performed in two stages. In the first stage, clustering technique (CT) has been used to extract representative features of EEG data. In the second stage, least square support vector machine (LS-SVM) is applied to the extracted features to classify two-class EEG signals. To demonstrate the effectiveness of the proposed method, several experiments have been conducted on three publicly available benchmark databases, one for epileptic EEG data, one for mental imagery tasks EEG data and another one for motor imagery EEG data. Our proposed approach achieves an average sensitivity, specificity and classification accuracy of 94.92%, 93.44% and 94.18%, respectively, for the epileptic EEG data; 83.98%, 84.37% and 84.17% respectively, for the motor imagery EEG data; and 64.61%, 58.77% and 61.69%, respectively, for the mental imagery tasks EEG data. The performance of the CT-LS-SVM algorithm is compared in terms of classification accuracy and execution (running) time with our previous study where simple random sampling with a least square support vector machine (SRS-LS-SVM) was employed for EEG signal classification. We also compare the proposed method with other existing methods in the literature for the three databases. The experimental results show that the proposed algorithm can produce a better classification rate than the previous reported methods and takes much less execution time compared to the SRS-LS-SVM technique. The research findings in this paper indicate that the proposed approach is very efficient for classification of two-class EEG signals.     

基于支持向量机的思维脑电信号特征分类研究

探索一种实用的基于想象运动思维脑电的脑-机接口(BCI)方式,为实现BCI应用奠定比较坚实的理论和实验基础。对6名受试者进行三种不同时段(箭头出现2s、1s和0s后提示按键)情况下想象左右手运动思维作业的信号采集实验,利用小波变换和支持向量机对实验数据进行离线处理。对三种情况下的延缓时间△t0、△t1和△t2分析发现:△t0与△t1和△t2之间都有显著性差别(p<0.05),而△t1与△t2之间没有显著差别(p>0.05);平均分类正确率分别达到68.00%、80.00%和56.67%(p<0.05);实际按键前0.5~1s左右,想象左右手运动的思维脑电特征信号都发生了明显改变。通过合理的实验设计获取的信号有助于识别正确率的提高,为BCI系统中思维任务的特征提取与识别分类提供了新思路和方法。     

A new approach for EEG signal classification of schizophrenic and control participants

This paper is concerned with a two stage procedure for analysis and classification of electroencephalogram (EEG) signals for twenty schizophrenic patients and twenty age-matched control participants. For each case, 20 channels of EEG are recorded. First, the more informative channels are selected using the mutual information techniques. Then, genetic programming is employed to select the best features from the selected channels. Several features including autoregressive model parameters, band power and fractal dimension are used for the purpose of classification. Both linear discriminant analysis (LDA) and adaptive boosting (Adaboost) are trained using tenfold cross validation to classify the reduced feature set and a classification accuracy of 85.90% and 91.94% is obtained by LDA and Adaboost, respectively. Another interesting observation from the channel selection procedure is that most of the selected channels are located in the prefrontal and temporal lobes confirming neuropsychological and neuroanatomical findings. The results obtained by the proposed approach are compared with a one stage procedure, the principal component analysis (PCA)-based feature selection, utilizing only 100 features selected from all channels. It is illustrated that the two stage procedure consisting of channel selection followed by feature reduction gives a more enhanced results in an efficient computation time.     

基于增强深度特征和TSK模糊分类器的癫痫脑电信号识别

识别癫痫脑电信号的关键在于获取有效的特征和构建可解释的分类器.为此,提出一种基于增强深度特征的TSK模糊分类器(ED-TSK-FC).首先,ED-TSK-FC使用一维卷积神经网络(1D-CNN)自动获取癫痫脑电信号的深度特征与潜在类别信息,并将深度特征和潜在类别信息合并为增强深度特征;其次,将增强深度特征作为ED-TSK-FC模糊规则前件与后件部分的训练变量,保证原始输入的深度特征及其潜在意义都出现在模糊规则中,进而对增强深度特征作出良好的解释;然后,采用岭回归极限学习算法对模糊规则的后件参数进行快速求解,在不显著降低分类准确度的情况下,ED-TSK-FC的廉价训练方法可以缩短模型的训练时间;最后,在Bonn癫痫数据集上,分别从分类性能、学习效率和可解释性3个方面,验证ED-TSK-FC的优越性.     

超核函数支持向量机

支持向量机是当前机器学习、模式识别和数据挖掘等领域的重要学习方法,核函数的构造是研究和应用支持向量机的关键问题.针对这一问题,提出了核函数构造的组合理论,定义了超核函数概念,并通过多项式组合现有核函数构造出一类超核函数.具体地,首先分析了一般核函数存在的过学习和欠学习现象,然后证明了组合理论构造的核函数的Mercer性质,并通过在仿真数据集和标准数据集上的对比实验,验证了超核函数的性能.理论分析和实验结果阐明了所提出的超核函数组合构造理论的合理性和有效性,开拓了模型选择组合方法的研究途径.