An improved multiclass support vector machine classifier using reduced hyper-plane with skewed binary tree

Classifiers in Automatic Speech Recognition (ASR) aims to improve the generalization ability of the machine learning and improve the recognition accuracy in noisy environments. This paper discusses the classification performance of Hidden Markov Models (HMM) and Support Vector Machines (SVM) applied to a wavelet front end based ASR. The experiments are performed on speaker independent TIMIT database which are trained in a clean environment and later tested in the presence of Additive White Gaussian Noise (AWGN) for various SNR levels using the HTK toolkit and SVM Light software tool. Experiments indicate that for large vocabulary the wavelet front end and the Multiclass SVM classifier with RBF kernel performs better than the conventional HMM classifier.

Wind speed prediction has been used in various fields such as Satellite launch, Air traffic control, Weather forecasting etc. Wind speed can be calculated by various atmospheric variables such as temperature, humidity, pressure, wind direction, etc. A number of methods have been proposed by various researchers to predict the wind speed. During the last few years a lot of research has been carried out to forecast the wind speed using several mathematical and biological methods. This paper first reviews various wind speed prediction techniques. It then explores the use of various multiclass Support Vector Machine (SVM) algorithms for wind speed prediction. It has been shown that multiclass Directed Acyclic Graph based Support Vector Machine algorithm produces better results than other multiclass SVM algorithms.

Detection and treatment of arrhythmias has become one of the main goals in cardiac care diagnosis provided by general practitioners. Electrocardiogram (ECG) analysis is one of the most commonly used tools to test and diagnose heart problems. Classification of ECG heartbeats enables the identification of specific arrhythmia or other heart conditions. This paper presents and contrasts the results from two effective ECG arrhythmia classification schemes. The first scheme consists of a principal component analysis (PCA) step for feature reduction at the input vector to the classifier, combined with soft independent modelling of class analogy (SIMCA). The second method uses a multi-class support vector machine (MSVM) classifier to differentiate between four different types of arrhythmia from ECG beats. The four types of beats include Normal (N), Premature Ventricular Contraction (PVC), and Atrial premature contraction (APC) and Right Bundle Branch Block Beat (RBBB). The time domain features were obtained from the St Petersburg INCART 12-lead Arrhythmia Database (incartdb). Between 10 and 30 Principal Components (PCs) were selected for reconstructing individual ECG beats and create the input vector to the classifier. The average classification accuracy of the proposed scheme is 76.83% and 98.33% using MSVM and SIMCA classifier respectively. The SIMCA classification algorithm provided better performance than the MSVM classifiers.

Support vector machine (SVM), which is based on statistical learning theory (SLT), has shown much better performance than most other existing machine learning methods, which are based on the traditional statistics. The original SVM was developed to solve the dichotomy classification problem. Various approaches have been presented to solve multi-class problems. Using multi-class SVM classifier we have obtained high class rate of 95.4% in remote sensing image classification. However for the class number of remote sensing image is much great, manually obtaining of training samples is a much time-consuming work. Hence, we present a multi-class SVM based semi-supervised approach. We choose the initial cluster centers manually first, then label the samples as the training ones automatically with fuzzy C-means clustering algorithm. It is believed that this method upgrades the classification efficiency greatly with practicable class rate.

The selection process for scholarship grantees at Universitas Muhammadiyah Yogyakarta (UMY) still utilizes the conventional method, namely Microsoft Excel. It is conducted by inputting all student data, then sorted from the highest to the lowest. Scholarships for prospective new students must be right on the target, meaning that they meet the criteria for students eligible for scholarships. It is intended not to harm other prospective students who should get scholarships. In previous studies, the classification process used many algorithms such as Naive Bayes, C.45, Decision Tree, k-Nearest Neighbor, and Support Vector Machine. The use of the Support Vector Machine algorithm employed a two-class classification. Support Vector Machine and Decision Tree algorithms are two classification methods that can obtain precise and accurate results. This study aims to use the Multiclass Support Vector Machine (LibSVM) algorithm to classify the achievement scholarship rankings for new students. The minimum amount of data used affected the classification results. From the 2015 to 2019 data, the highest amount of data was 2015, obtaining the highest accuracy result of 84.34% using the sigmoid kernel type and the k-fold value of 3. The classification was based on the entry system stages. PMDK stage 2 obtained an accuracy of 81.38%, with the most data amounting to 268 from stage 3.

Support vector machines (SVM) is originally designed for binary classification. To extend it to multi-class scenario, a typical conventional way is to decompose an M-class problem into a series of two-class problems, for which one-against-all is the earliest and one of the most widely used implementations. However, certain theoretical analysis reveals a drawback, i.e., the competence of each classifier is totally neglected when the results of classification from the multiple classifiers are combined for the final decision. To overcome this limitation, this paper introduces reliability measures into the multi-class framework. Two measures are designed: static reliability measure (SRM) and dynamic reliability measure (DRM). SRM works on a collective basis and yields a constant value regardless of the location of the test sample. DRM, on the other hand, accounts for the spatial variation of the classifier's performance. Based on these two reliability measures, a new decision strategy for the one-against-all method is proposed, which is tested on benchmark data sets and demonstrates its effectiveness.

This work presents synergistic approach for combining Directed Acyclic Graph (DAG), Binary Tree (BT) and Binary Decision Tree (BDT) based Support Vector Machine (SVM) algorithms for predicting wind speed. The proposed approach and individual algorithms are evaluated on wind speed data that has many samples divided into training and test data sets. The proposed approach of using the synergistic approach produces better results than Directed Acyclic Graph, Binary Tree, and Binary Decision Tree (BDT) based multiclass SVM algorithms individually.

Multi class SVM algorithm with active learning for network traffic classification

The setting of parameters in the support vector machines (SVMs) is very important with regard to its accuracy and efficiency. In this paper, we employ the firefly algorithm to train all parameters of the SVM simultaneously, including the penalty parameter, smoothness parameter, and Lagrangian multiplier. The proposed method is called the firefly-based SVM (firefly-SVM). This tool is not considered the feature selection, because the SVM, together with feature selection, is not suitable for the application in a multiclass classification, especially for the one-against-all multiclass SVM. In experiments, binary and multiclass classifications are explored. In the experiments on binary classification, ten of the benchmark data sets of the University of California, Irvine (UCI), machine learning repository are used; additionally the firefly-SVM is applied to the multiclass diagnosis of ultrasonic supraspinatus images. The classification performance of firefly-SVM is also compared to the original LIBSVM method associated with the grid search method and the particle swarm optimization based SVM (PSO-SVM). The experimental results advocate the use of firefly-SVM to classify pattern classifications for maximum accuracy.

In the context of improved navigation for micro aerial vehicles, a new scene recognition visual descriptor, called spatial color gist wavelet descriptor (SCGWD), is proposed. SCGWD was developed by combining proposed Ohta color-GIST wavelet descriptors with census transform histogram (CENTRIST) spatial pyramid representation descriptors for categorizing indoor versus outdoor scenes. A binary and multiclass support vector machine (SVM) classifier with linear and non-linear kernels was used to classify indoor versus outdoor scenes and indoor scenes, respectively. In this paper, we have also discussed the feature extraction methodology of several, state-of-the-art visual descriptors, and four proposed visual descriptors (Ohta color-GIST descriptors, Ohta color-GIST wavelet descriptors, enhanced Ohta color histogram descriptors, and SCGWDs), in terms of experimental perspectives. The proposed enhanced Ohta color histogram descriptors, Ohta color-GIST descriptors, Ohta color-GIST wavelet descriptors, SCGWD, and state-of-the-art visual descriptors were evaluated, using the Indian Institute of Technology Madras Scene Classification Image Database two, an Indoor-Outdoor Dataset, and the Massachusetts Institute of Technology indoor scene classification dataset [(MIT)-67]. Experimental results showed that the indoor versus outdoor scene recognition algorithm, employing SVM with SCGWDs, produced the highest classification rates (CRs)—95.48% and 99.82% using radial basis function kernel (RBF) kernel and 95.29% and 99.45% using linear kernel for the IITM SCID2 and Indoor-Outdoor datasets, respectively. The lowest CRs—2.08% and 4.92%, respectively—were obtained when RBF and linear kernels were used with the MIT-67 dataset. In addition, higher CRs, precision, recall, and area under the receiver operating characteristic curve values were obtained for the proposed SCGWDs, in comparison with state-of-the-art visual descriptors.

Rock-type classification is a challenging and difficult job due to the heterogeneous properties of rocks. In this paper, an image-based rock-type analysis and classification method is proposed. The study was conducted at a limestone mine in western India using stratified random sampling from a case study mine. The analysis of collected sample images was performed in laboratory. Color, morphology, and textural features were extracted from the captured image and a total of 189 features were recorded. The multi-class support vector machine (SVM) algorithm was then applied for rock-type classification. The hyper-parameters and the number of input features of the SVM model were selected by genetic algorithm. The results revealed that the SVM model performed best when 40 features were selected out of the 189 extracted features. The results demonstrated that the overall accuracy of the proposed technique for rock type classification is 96.2 %. A comparative study shows that the proposed SVM model performed better than a competing neural network model in this case study mine.

Image annotation systems aim at automatically annotating images with some predefined keywords. In this paper, we propose an automatic image annotation approach by incorporating word correlations into multi-class support vector machine (SVM). At first, each image is segmented into five fixed-size blocks or tiles and MPEG-7 visual descriptors are applied to represent color and texture features of blocks. Keywords are manually assigned to every block of training images. Then, multi-class SVM classifier is trained for semantic concepts. Word or concept correlations are computed by a co-occurrence matrix. The probability outputs from SVM and word correlations are combined to obtain the final results. The minimal-redundancy-maximum-relevance (mRMR) method is used to reduce feature dimensions. The experiments on Corel 5000 dataset demonstrate our approach is effective and efficient.

Security assessment and classification are the major concerns in real-time operation of electric power systems. This paper proposes a multiclass support vector machine (SVM) classifier for static and transient security assessment and classification. A straightforward and quick procedure called the sequential forward selection method is used for a feature selection process. The security status of any given operating condition is classified into four modes, viz., secure, critically secure, insecure, and highly insecure, based on the computation of a security index. The proposed SVM-based pattern classifier system is implemented and tested on standard benchmark systems. The simulation results of the multiclass SVM classifier are compared with least-squares, probabilistic neural network, extreme learning machine, and extreme SVM classifiers. The feasibility of implementation of the proposed classifier system for online security evaluation is also discussed.

Temperature modulation of electronic nose combined with multi-class support vector machine classification for identifying export caraway cultivars

In view of the batch implementations of standard support vector machine must be retrained from scratch every time when the training set is incremental modified, an incremental learning algorithm based on least squares twin multi-class classification support vector machine (ILST-KSVC) is proposed by solving two inverse matrix. The method will be applied on online environment to update initial data, which avoided cumbersome double counting. ILST-KSVC inherited the advantages of the basic algorithm and has some merits of Least square twin support vector machine for excellent performance on training speed and support vector classification regression for K-class’s well classification accuracy. The result will be confirmed no matter in low dimension or in high dimension in UCI datasets.