Doppler ultrasound signals analysis using multiclass support vector machines with error correcting output codes

Feature saliency using signal-to-noise ratios in automated diagnostic systems developed for Doppler ultrasound signals

This paper presents the use of multiclass support vector machines (SVMs) for diagnosis of spirometric patterns (normal, restrictive, obstructive). The SVM decisions were fused using the error correcting output codes (ECOC). The multiclass SVM with the ECOC was trained on three spirometric parameters (forced expiratory volume in 1s--FEV1, forced vital capacity--FVC and FEV1/FVC ratio). The total classification accuracy of the SVM is 97.32%. The obtained results confirmed the validity of the SVMs to help in clinical decision-making.

A recurrent neural network classifier for Doppler ultrasound blood flow signals

Face recognition (FR) has drawn considerable interest and attention in the area of pattern recognition. FR is still a challenging task in real time applications even though they are a number of face recognition algorithms which are available and work in various constrained environment. The paper proposes a FR algorithm using Modified Local Ternary Pattern (MLTP) with multi class Support Vector Machine (SVM) classifier. The MLTP features of the face images are classified by an Error-Correcting Output Code (ECOC) multiclass model with SVM. The proposed method is tested on six standard face databases. The experimental results have been demonstrated that the performance of MLTP with SVM can achieve higher recognition accuracy compared to the conventional methods.

Comparison between error correcting output codes and fuzzy support vector machines

This study presents a multi-class least square support vector machines (LS-SVM)-based classifier for transformer fault diagnosis. First, the original binary classifier is extended for multi-class classification that is common in fault diagnosis by using combination schemes, that is, the minimal output coding, error correcting output codes, one-against-one and one-against-all schemes. Second, the algorithm of particle swarm optimisation is implemented to select the optimal feature parameters for the multi-class LS-SVM classifiers. Then the effectiveness of the proposed approach is verified on the basis of the experiments on benchmark classification data and real-world transformer data. For comparison purpose, three widely used transformer diagnosis methods such as the IEC criteria, back propagation neural network and standard support vector machines are utilised. The results show the proposed approach has a better performance both in training and testing accuracies.

Spectral analysis of internal carotid arterial Doppler signals using FFT, AR, MA, and ARMA methods

Abstract. Image processing has been one of the efficient technologies for GIS data requisition. Support Vector Machines (SVMs) have peculiar advantages in handling problems with small sample sizes, nonlinearity, and high dimensionality. However, SVMs can only solve two-class problems while multi-class decision is impossible. Error correcting output coding (ECOC) SVMs enhance the ability of fault tolerance when solving multi-class classification problems, which makes ECOC SVMs suitable for remote sensing image classification. In this paper, the generalization ability of ECOC SVMs is discussed. ECOC SVMs with optimum coding matrices are selected by experiment, and applied to remote sensing image classification. Experimental results show that, compared with Conventional multi-class classification methods, less SVM sub-classifiers are needed for ECOC SVMs in remote sensing image classification, and the classification accuracy is also improved.

Many research on how the human brain works has been done in the last century. The use of electroencephalogram signal generated from quantifying the brain wave have been developed in many areas including the development of brain computer interface (BCI) concept. One type of BCI that interesting for the future use is motor imagery (MI) based-BCI which only requiring imagination of a person to control an object. This study proposed a feature extraction in eight different channels using discrete wavelet (DWT) coefficients. The wavelet coefficient is transformed to frequency domain using discrete fourier transform (DFT) and then average power spectrum is calculated. Level 5 of detail component of the DWT is chosen because from 512Hz sampling frequency (8 - 16Hz), it resemble mu rhythm of brain wave (8 - 12Hz) which affected from motor imagery activity. The classification of three classes, which are imagination of right body movement, left movement, and random word using multiclass support vector machine (SVM) shows a promising result with sensitivity of 96.88%, 86.12% and 52.78% from three different subjects.

A new approach based on the implementation of support vector machine (SVM) with the error correcting output codes (ECOC) is presented for recognition of multi-class protein folds. The experimental show that the proposed method can improve prediction accuracy by 4%-10% on two datasets containing 27 SCOP folds.

The Error-Correcting Output Codes (ECOC) method reduces the multi-class learning problem into a series of binary classifiers. In this paper, we propose a modified Hadamard-type ECOC method. This method uses both N’th order and N/2’th-order Hadamard matrix to construct error correcting output codes, which is called Hybrid Hadamard ECOC. Experiments based on dichotomizers of Support Vector Machines (SVM) have been carried out to evaluate the performance of the proposed method. When compared to normal Hadamard ECOC, computation of the method is reduced greatly while the accuracy of classification only drops slightly.

Automatic Speech Recognition (ASR) is essentially a problem of pattern classification, however, the time dimension of the speech signal has prevented to pose ASR as a simple static classification problem. Support Vector Machine (SVM) classifiers could provide an appropriate solution, since they are very well adapted to high-dimensional classification problems. Nevertheless, the use of SVMs for ASR is by no means straightforward, mainly because SVM classifiers require an input of fixed-dimension. In this paper we study the use of a HMM-based segmentation as a mean to get the fixed-dimension input vectors required by SVMs, in a problem of isolated-digit recognition. Different configurations for all the parameters involved have been tested. Also, we deal with the problem of multi-class classification (as SVMs are initially binary classifers), studying two of the most popular approaches: 1-vs-all and 1-vs-1.

Error-Correcting Output Codes (ECOC) provides an effective solution for the multiclass classification problem by decomposing a multiclass problem into a set of binary class problems. In an ECOC algorithm, the design of coding matrix is the key to its performance. In this paper, we propose a Genetic Programming (GP) based ECOC algorithm, aiming to produce optimal coding matrices through the evolutionary process. In our GP, each terminal node denotes a column in the coding matrix, and each nonterminal node represents an operator, which combines the columns represented by its terminal nodes. In this way, an individual is interpreted as a coding matrix, and a set of operators are proposed to exchange information between column pairs, so as to produce new columns. Feature selection methods are also integrated into the terminal nodes, so that individuals are dynamically assigned to optimal feature subspaces for diverse classification problems. With evolutionary operators, offspring with high discriminant capability would be produced in the evolution. Our experiments compare our algorithm with other 7 classic ECOC algorithms with the deployment of diverse basic classifiers based on a set of UCI data sets, and results prove the superiority and robustness of our algorithm.

Lung cancer has been a leading cause of cancer-related mortality in recent years, and early detection can increase patients’ chances of recovery. Machine learning and image processing may be used to analyze Computed Tomography (CT) scans for signs of lung cancer; by integrating several machine learning models, the accuracy of lung cancer diagnoses can be increased. In this paper, we propose a method that introduces a segmentation algorithm based on Social Spider Optimization (SSO) to detect suspicious regions in the CT image. The proposed method uses a combination of Error Correcting Output Codes (ECOC) and Support Vector Machine (SVM) to classify suspicious regions and diagnose lung cancer. The efficiency has been evaluated and compared with previous works. The results show that the proposed method can diagnose lung cancer with an average accuracy of 96.67% and can be used as an efficient tool for assisting specialists in diagnosing lung cancer.

Recently, focus on maintenance strategies has been shifted towards prognostic health management (PHM) and a number of state of the art algorithms based on data-driven prognostics have been developed to predict the health states of degrading components based on sensory data. Amongst these algorithms, Multiclass Support Vector Machines (MC-SVM) has gained popularity due to its relatively high classification accuracy, ability to classify multiple patterns and capability to handle noisy / incomplete data. However, its application is limited by the difficulty in determining the required kernel function and penalty parameters. To address this problem, this paper proposes a hybrid differential evolution – particle swarm optimization (DE-PSO) algorithm to optimize the MC-SVM kernel function and penalty parameters. The differential algorithm (DE) obtains the search limit for the SVM parameters, while the particle swarm optimization algorithm (PSO) determines the global optimum parameters for a given training data set. Since degrading machinery components display several degradation stages in their lifetime, the MC-SVM trained with optimum parameters are used to estimate the health states of a degrading machinery component, from which the remaining useful life (RUL) is predicted. This method improves the classification accuracy of MC-SVM in predicting the health states of a machinery component and consequently increases the accuracy of RUL predictions. The feasibility of the method is validated using bearing prognostic run-to-failure data obtained from NASA public data repository. A comparative study between MC-SVM with parameters obtained using simple grid search with n-fold cross validation and MC- SVM with DE-PSO based on prognostic performance metrics reveals that the proposed method has better performance, with all the cases considered falling within a 10 % error margin. The method also outperforms other soft computing methods proposed in literature.