ECG arrhythmia classification based on optimum-path forest

An important tool for the heart disease diagnosis is the analysis of electrocardiogram (ECG) signals, since the non-invasive nature and simplicity of the ECG exam. According to the application, ECG data analysis consists of steps such as preprocessing, segmentation, feature extraction and classification aiming to detect cardiac arrhythmias (i.e., cardiac rhythm abnormalities). Aiming to made a fast and accurate cardiac arrhythmia signal classification process, we apply and analyze a recent and robust supervised graph-based pattern recognition technique, the optimum-path forest (OPF) classifier. To the best of our knowledge, it is the first time that OPF classifier is used to the ECG heartbeat signal classification task. We then compare the performance (in terms of training and testing time, accuracy, specificity, and sensitivity) of the OPF classifier to the ones of other three well-known expert system classifiers, i.e., support vector machine (SVM), Bayesian and multilayer artificial neural network (MLP), using features extracted from six main approaches considered in literature for ECG arrhythmia analysis. In our experiments, we use the MIT-BIH Arrhythmia Database and the evaluation protocol recommended by The Association for the Advancement of Medical Instrumentation. A discussion on the obtained results shows that OPF classifier presents a robust performance, i.e., there is no need for parameter setup, as well as a high accuracy at an extremely low computational cost. Moreover, in average, the OPF classifier yielded greater performance than the MLP and SVM classifiers in terms of classification time and accuracy, and to produce quite similar performance to the Bayesian classifier, showing to be a promising technique for ECG signal analysis.     

An extreme learning machine approach for speaker recognition

Over the last two decades, automatic speaker recognition has been an interesting and challenging problem to speech researchers. It can be classified into two different categories, speaker identification and speaker verification. In this paper, a new classifier, extreme learning machine, is examined on the text-independent speaker verification task and compared with SVM classifier. Extreme learning machine (ELM) classifiers have been proposed for generalized single hidden layer feedforward networks with a wide variety of hidden nodes. They are extremely fast in learning and perform well on many artificial and real regression and classification applications. The database used to evaluate the ELM and SVM classifiers is ELSDSR corpus, and the Mel-frequency Cepstral Coefficients were extracted and used as the input to the classifiers. Empirical studies have shown that ELM classifiers and its variants could perform better than SVM classifiers on the dataset provided with less training time.     

Application of complex extreme learning machine to multiclass classification problems with high dimensionality: A THz spectra classification problem

We extend extreme learning machine (ELM) classifiers to complex Reproducing Kernel Hilbert Spaces (RKHS) where the input/output variables as well as the optimization variables are complex-valued. A new family of classifiers, called complex-valued ELM (CELM) suitable for complex-valued multiple-input–multiple-output processing is introduced. In the proposed method, the associated Lagrangian is computed using induced RKHS kernels, adopting a Wirtinger calculus approach formulated as a constrained optimization problem similarly to the conventional ELM classifier formulation. When training the CELM, the Karush–Khun–Tuker (KKT) theorem is used to solve the dual optimization problem that consists of satisfying simultaneously smallest training error as well as smallest norm of output weights criteria. The proposed formulation also addresses aspects of quaternary classification within a Clifford algebra context. For 2D complex-valued inputs, user-defined complex-coupled hyper-planes divide the classifier input space into four partitions. For 3D complex-valued inputs, the formulation generates three pairs of complex-coupled hyper-planes through orthogonal projections. The six hyper-planes then divide the 3D space into eight partitions. It is shown that the CELM problem formulation is equivalent to solving six real-valued ELM tasks, which are induced by projecting the chosen complex kernel across the different user-defined coordinate planes. A classification example of powdered samples on the basis of their terahertz spectral signatures is used to demonstrate the advantages of the CELM classifiers compared to their SVM counterparts. The proposed classifiers retain the advantages of their ELM counterparts, in that they can perform multiclass classification with lower computational complexity than SVM classifiers. Furthermore, because of their ability to perform classification tasks fast, the proposed formulations are of interest to real-time applications.     

基于支持向量机和距离度量的管道内表面图像分类方法研究

针对管道内表面图像的分类问题，提出了一种将支持向量机和距离度量相结合，构成组合分类器的分类方法。分类时先采用距离度量进行前级分类，符合条件则给出分类结果，否则拒识并转入SVM分类器进行分类。该方法充分利用了SVM识别率高和距离度量速度快的优点，并且利用距离度量的结果去指导SVM的训练和测试。实验表明本方法具有较高的效率和识别精度，进一步提高了系统的识别率和容噪性能。     

基于AdaBoost特征约减的入侵检测分类方法

提出一种基于AdaBoost的入侵特征约减算法,利用该算法约减入侵特征中的冗余特征,构造Ada-加权和Ada-域值分类器,并与支持向量机分类器进行对比。设计并实现Linux实时入侵检测实验平台,并将特征约减算法和3种分类方法应用于该平台。实验结果表明,由特征约减算法挑选出来的入侵特征集较优,Ada-加权和Ada-域值分类器的分类效果优于支持向量机分类器,且Ada-域值分类器在测试集上的检测性能最佳。     

A comparative study of classifier ensembles for bankruptcy prediction

The aim of bankruptcy prediction in the areas of data mining and machine learning is to develop an effective model which can provide the higher prediction accuracy. In the prior literature, various classification techniques have been developed and studied, in/with which classifier ensembles by combining multiple classifiers approach have shown their outperformance over many single classifiers. However, in terms of constructing classifier ensembles, there are three critical issues which can affect their performance. The first one is the classification technique actually used/adopted, and the other two are the combination method to combine multiple classifiers and the number of classifiers to be combined, respectively. Since there are limited, relevant studies examining these aforementioned disuses, this paper conducts a comprehensive study of comparing classifier ensembles by three widely used classification techniques including multilayer perceptron (MLP) neural networks, support vector machines (SVM), and decision trees (DT) based on two well-known combination methods including bagging and boosting and different numbers of combined classifiers. Our experimental results by three public datasets show that DT ensembles composed of 80–100 classifiers using the boosting method perform best. The Wilcoxon signed ranked test also demonstrates that DT ensembles by boosting perform significantly different from the other classifier ensembles. Moreover, a further study over a real-world case by a Taiwan bankruptcy dataset was conducted, which also demonstrates the superiority of DT ensembles by boosting over the others.     

便携心电传感器的心房肥大诊断研究

基于便携式传感器的模式识别在心电(ECG)监护领域具有广泛的应用前景,并且在心律不齐、心肌梗塞、心室肥大等心电的识别算法上都已有大量的研究与应用,但在心房肥大诊断上却未有模式识别相关的研究成果。心房肥大病症的心电数据量不足给研究造成重大障碍,部分分类器无法适应小样本训练下的分类。针对小样本训练进行研究,对比了不同分类方法,显示了基于统计模式识别的支持向量机(SVM)应用于心房肥大的应用潜力。另外,由于不同个体的心房肥大心电存在差异,在实际应用环境中,SVM存在无法良好泛化的问题,存在类别错分的医学风险。针对类别错分情况,采用分类器融合的方法改进分类器,提出了在SVM分类器输出端增加了拒绝域的分类器(SVM-R)的方法。实验结果表明:SVMR有较高的分类准确率与诊断可信度。     

一种新型的两级指纹分类方法

提出了一种利用隐马尔可夫模型(HMM)和支持向量机(SVM)的两级指纹分类新方法. 该方法采用指纹编码(FingerCode)作为指纹的特征表述,在对分类器进行训练之后,首先用5个 伪二维HMM对待分类指纹进行类别初选,确定最可能的两种指纹分类结果,再用相应的SVM 分类器做最终判决.最后使用NIST-4数据库中的2000幅指纹和CQU-VERIDICOM数据库的 1000幅指纹对该方法进行了实验,其分类的准确性为91%,连续性为93.7%,这证明了该方法的 有效性.     

基于证据理论的多类分类支持向量机集成

针对多类分类问题,研究支持向量机集成中的分类器组合架构与方法.分析已有的多类级和两类级支持向量机集成架构的不足后,提出两层的集成架构.在此基础上,研究基于证据理论的支持向量机度量层输出信息融合方法,针对一对多与一对一两种多类扩展策略,分别定义基本概率分配函数,并根据证据冲突程度采用不同的证据组合规则.在一对多策略下,采用经典的Dempster规则;在一对一策略下则提出一条新的规则,以组合冲突严重的证据.实验表明,两层架构优于多类级架构,证据理论方法能有效地利用两类支持向量机的度量层输出信息,取得了满意的结果.     

An Iterative Method for Deciding SVM and Single Layer Neural Network Structures

We present two new classifiers for two-class classification problems using a new Beta-SVM kernel transformation and an iterative algorithm to concurrently select the support vectors for a support vector machine (SVM) and the hidden units for a single hidden layer neural network to achieve a better generalization performance. To construct the classifiers, the contributing data points are chosen on the basis of a thresholding scheme of the outputs of a single perceptron trained using all training data samples. The chosen support vectors are used to construct a new SVM classifier that we call Beta-SVN. The number of chosen support vectors is used to determine the structure of the hidden layer in a single hidden layer neural network that we call Beta-NN. The Beta-SVN and Beta-NN structures produced by our method outperformed other commonly used classifiers when tested on a 2-dimensional non-linearly separable data set.     

Optimization techniques for improving power quality data mining using wavelet packet based support vector machine

This paper aims at automatic classification of power quality events using Wavelet Packet Transform (WPT) and Support Vector Machines (SVM). The features of the disturbance signals are extracted using WPT and given to the SVM for effective classification. Recent literature dealing with power quality establishes that support vector machine methods generally outperform traditional statistical and neural methods in classification problems involving power disturbance signals. However, the two vital issues namely the determination of the most appropriate feature subset and the model selection, if suitably addressed, could pave way for further improvement of their performances in terms of classification accuracy and computation time. This paper addresses these issues through a classification system using two optimization techniques, the genetic algorithms and simulated annealing. This system detects the best discriminative features and estimates the best SVM kernel parameters in a fully automatic way. Effectiveness of the proposed detection method is shown in comparison with the conventional parameter optimization methods discussed in literature like grid search method, neural classifiers like Probabilistic Neural Network (PNN), fuzzy k-nearest neighbor classifier (FkNN) and hence proved that the proposed method is reliable as it produces consistently better results.     

Electrocardiogram beat classification using empirical mode decomposition and multiclass directed acyclic graph support vector machine

In this paper, a classifier motivated from statistical learning theory, i.e., support vector machine, with a new approach based on multiclass directed acyclic graph has been proposed for classification of four types of electrocardiogram signals. The motivation for selecting Directed Acyclic Graph Support Vector Machine (DAGSVM) is to have more accurate classifier with less computational cost. Empirical mode decomposition and subsequently singular value decomposition have been used for computing the feature vector matrix. Further, fivefold cross-validation and particle swarm optimization have been used for optimal selection of SVM model parameters to improve the performance of DAGSVM. A comparison has been made between proposed algorithm and other two classifiers, i.e., K-Nearest Neighbor (KNN) and Artificial Neural Network (ANN). The DAGSVM has yielded an average accuracy of 98.96% against 95.83% and 96.66% for the KNN and the ANN, respectively. The results obtained clearly confirm the superiority of the DAGSVM approach over other classifiers.     

Reducing SVM classification time using multiple mirror classifiers.

We propose an approach that uses mirror point pairs and a multiple classifier system to reduce the classification time of a support vector machine (SVM). Decisions made with multiple simple classifiers formed from mirror pairs are integrated to approximate the classification rule of a single SVM. A coarse-to-fine approach is developed for selecting a given number of member classifiers. A clustering method, derived from the similarities between classifiers, is used for a coarse selection. A greedy strategy is then used for fine selection of member classifiers. Selected member classifiers are further refined by finding a weighted combination with a perceptron. Experiment results show that our approach can successfully speed up SVM decisions while maintaining comparable classification accuracy.     

Force based tool wear monitoring system for milling process based on relevance vector machine

The monitoring of tool wear status is paramount for guaranteeing the workpiece quality and improving the manufacturing efficiency. In some cases, classifier based on small training samples is preferred because of the complex tool wear process and time consuming samples collection process. In this paper, a tool wear monitoring system based on relevance vector machine (RVM) classifier is constructed to realize multi categories classification of tool wear status during milling process. As a Bayesian algorithm alternative to the support vector machine (SVM), RVM has stronger generalization ability under small training samples. Moreover, RVM classifier results in fewer relevance vectors (RVs) compared with SVM classifier. Hence, it can be carried out much faster compared to the SVM. To show the advantages of the RVM classifier, milling experiment of Titanium alloy was carried out and the multi categories classification of tool wear status under different numbers of training samples and test samples are realized by using SVM and RVM classifier respectively. The comparison of SVM with RVM shows that the RVM can get more accurate results under different number of small training samples. Moreover, the speed of classification is faster than SVM. This method casts some new lights on the industrial environment of the tool condition monitoring.     

A telemedicine tool to detect pulmonary pathology using computerized pulmonary acoustic signal analysis

BackgroundDetection and monitoring of respiratory related illness is an important aspect in pulmonary medicine. Acoustic signals extracted from the human body are considered in detection of respiratory pathology accurately.ObjectivesThe aim of this study is to develop a prototype telemedicine tool to detect respiratory pathology using computerized respiratory sound analysis.MethodsAround 120 subjects (40 normal, 40 continuous lung sounds (20 wheeze and 20 rhonchi)) and 40 discontinuous lung sounds (20 fine crackles and 20 coarse crackles) were included in this study. The respiratory sounds were segmented into respiratory cycles using fuzzy inference system and then S-transform was applied to these respiratory cycles. From the S-transform matrix, statistical features were extracted. The extracted features were statistically significant with p < 0.05. To classify the respiratory pathology KNN, SVM and ELM classifiers were implemented using the statistical features obtained from of the data.ResultsThe validation showed that the classification rate for training for ELM classifier with RBF kernel was high compared to the SVM and KNN classifiers. The time taken for training the classifier was also less in ELM compared to SVM and KNN classifiers. The overall mean classification rate for ELM classifier was 98.52%.ConclusionThe telemedicine software tool was developed using the ELM classifier. The telemedicine tool has performed extraordinary well in detecting the respiratory pathology and it is well validated.     

Support vector learning for fuzzy rule-based classification systems

To design a fuzzy rule-based classification system (fuzzy classifier) with good generalization ability in a high dimensional feature space has been an active research topic for a long time. As a powerful machine learning approach for pattern recognition problems, the support vector machine (SVM) is known to have good generalization ability. More importantly, an SVM can work very well on a high- (or even infinite) dimensional feature space. This paper investigates the connection between fuzzy classifiers and kernel machines, establishes a link between fuzzy rules and kernels, and proposes a learning algorithm for fuzzy classifiers. We first show that a fuzzy classifier implicitly defines a translation invariant kernel under the assumption that all membership functions associated with the same input variable are generated from location transformation of a reference function. Fuzzy inference on the IF-part of a fuzzy rule can be viewed as evaluating the kernel function. The kernel function is then proven to be a Mercer kernel if the reference functions meet a certain spectral requirement. The corresponding fuzzy classifier is named positive definite fuzzy classifier (PDFC). A PDFC can be built from the given training samples based on a support vector learning approach with the IF-part fuzzy rules given by the support vectors. Since the learning process minimizes an upper bound on the expected risk (expected prediction error) instead of the empirical risk (training error), the resulting PDFC usually has good generalization. Moreover, because of the sparsity properties of the SVMs, the number of fuzzy rules is irrelevant to the dimension of input space. In this sense, we avoid the "curse of dimensionality." Finally, PDFCs with different reference functions are constructed using the support vector learning approach. The performance of the PDFCs is illustrated by extensive experimental results. Comparisons with other methods are also provided.     

基于支持向量机和距离度量的纹理分类

针对图象纹理分类问题，提出了一种将支持向量机和距离度量相结合，以构成两级组合分类器的分类方法，用该方法分类时，先采用距离度量进行前级分类，然后根据图象的纹理统计特征，采用欧氏距离来度量图象之间的相似性，若符合条件，则给出分类结果，否则拒识，并转入后级分类器，而后级分类器则采用一种新的模式分类方法－支持向量机进行分类，该组合分类方法不仅充分利用了支持向量机识别率高和距离度量速度快的优点，并且还利用距离度量的结果去指导支持向量机的训练和测试，由纹理图象分类的实验表明，该算法具有较高的效率和识别精度，同时也对推动支持向量机这一新的模式分类方法的实际应用具有积极意义。     

Land-cover classification of partly missing data using support vector machines

Land-cover classification based on multi-temporal satellite images for scenarios where parts of the data are missing due to, for example, clouds, snow or sensor failure has received little attention in the remote-sensing literature. The goal of this article is to introduce support vector machine (SVM) methods capable of handling missing data in land-cover classification. The novelty of this article consists of combining the powerful SVM regularization framework with a recent statistical theory of missing data, resulting in a new method where an SVM is trained for each missing data pattern, and a given incomplete test vector is classified by selecting the corresponding SVM model. The SVM classifiers are evaluated on Landsat Enhanced Thematic Mapper Plus (ETM?+?) images covering a scene of Norwegian mountain vegetation. The results show that the proposed SVM-based classifier improves the classification accuracy by 5–10% compared with single image classification. The proposed SVM classifier also outperforms recent non-parametric k-nearest neighbours (k-NN) and Parzen window density-based classifiers for incomplete data by about 3%. Moreover, since the resulting SVM classifier may easily be implemented using existing SVM libraries, we consider the new method to be an attractive choice for classification of incomplete data in remote sensing.     

An efficient classifier to diagnose of schizophrenia based on the EEG signals

In this paper, electroencephalogram (EEG) signals of 13 schizophrenic patients and 18 age-matched control participants are analyzed with the objective of classifying the two groups. For each case, multi-channels (22 electrodes) scalp EEG is recorded. Several features including autoregressive (AR) model parameters, band power and fractal dimension are extracted from the recorded signals. Leave-one (participant)-out cross validation is used to have an accurate estimation for the separability of the two groups. Boosted version of Direct Linear Discriminant Analysis (BDLDA) is selected as an efficient classifier which applied on the extracted features. To have comparison, classifiers such as standard LDA, Adaboost, support vector machine (SVM), and fuzzy SVM (FSVM) are applied on the features. Results show that the BDLDA is more discriminative than others such that their classification rates are reported 87.51%, 85.36% and 85.41% for the BDLDA, LDA, Adaboost, respectively. Results of SVM and FSVM classifiers were lower than 50% accuracy because they are more sensitive to outlier instances. In order to determine robustness of the suggested classifier, noises with different amplitudes are added to the test feature vectors and robustness of the BDLDA was higher than the other compared classifiers.