scholarly journals Performance of Principal Component Analysis and Orthogonal Least Square on Optimized Feature Set in Classifying Asphyxiated Infant Cry Using Support Vector Machine

2018 ◽  
Vol 9 (1) ◽  
pp. 139
Author(s):  
R. Sahak ◽  
W. Mansor ◽  
Khuan Y. Lee ◽  
A. Zabidi
Keyword(s):  
Principal Component Analysis ◽  
Support Vector Machine ◽  
Classification Accuracy ◽  
Computation Time ◽  
Principal Component ◽  
Component Analysis ◽  
Least Square ◽  
Support Vector ◽  
Infant Cry ◽  
Orthogonal Least Square

<p>An investigation into optimized support vector machine (SVM) integrated with principal component analysis (PCA) and orthogonal least square (OLS) in classifying asphyxiated infant cry was performed in this study. Three approaches were used in the classification; SVM, PCA-SVM, and OLS-SVM. Various numbers of features extracted from Mel-frequency Cepstral coefficient (MFCC) were tested to obtain the optimal parameters of SVM kernels. Once the optimal feature set is obtained, PCA and OLS selected the most significant features and the optimized SVM then classified the selected cry patterns. In PCA-SVM, eigenvalue-one-criterion (EOC), cumulative percentage variance (CPV) and the Scree test (SCREE) were used to select the most significant features. SVM with radial basis function (RBF) kernel was chosen in the classification stage. The classification accuracy and computation time were computed to evaluate the performance of each method. The best method for classifying asphyxiated infant cry is PCA-SVM with EOC since it produces the highest classification accuracy which is 94.84%. Using PCA-SVM, the classification process was performed in 1.98s only. The results also show that employing feature selection techniques could enhance the classifier performance.</p>

A soft computing approach for diabetes disease classification

2016 ◽  
Vol 24 (4) ◽  
pp. 379-393 ◽  
Author(s):  
Mehrbakhsh Nilashi ◽  
Othman Bin Ibrahim ◽  
Abbas Mardani ◽  
Ali Ahani ◽  
Ahmad Jusoh
Keyword(s):  
Principal Component Analysis ◽  
Support Vector Machine ◽  
Intelligent System ◽  
Computation Time ◽  
Principal Component ◽  
Component Analysis ◽  
Noise Removal ◽  
Disease Classification ◽  
Machine Learning Techniques ◽  
Support Vector

As a chronic disease, diabetes mellitus has emerged as a worldwide epidemic. The aim of this study is to classify diabetes disease by developing an intelligence system using machine learning techniques. Our method is developed through clustering, noise removal and classification approaches. Accordingly, we use expectation maximization, principal component analysis and support vector machine for clustering, noise removal and classification tasks, respectively. We also develop the proposed method for incremental situation by applying the incremental principal component analysis and incremental support vector machine for incremental learning of data. Experimental results on Pima Indian Diabetes dataset show that proposed method remarkably improves the accuracy of prediction and reduces computation time in relation to the non-incremental approaches. The hybrid intelligent system can assist medical practitioners in the healthcare practice as a decision support system.

scholarly journals An Intelligent System Approach for Asthma Prediction in Symptomatic Preschool Children

2013 ◽  
Vol 2013 ◽  
pp. 1-6 ◽  
Author(s):  
E. Chatzimichail ◽  
E. Paraskakis ◽  
M. Sitzimi ◽  
A. Rigas
Keyword(s):  
Principal Component Analysis ◽  
Support Vector Machine ◽  
Support Vector Machine Classifier ◽  
Outcome Prediction ◽  
Intelligent System ◽  
Principal Component ◽  
Component Analysis ◽  
Least Square ◽  
Support Vector ◽  
Asthma Outcome

Objectives. In this study a new method for asthma outcome prediction, which is based on Principal Component Analysis and Least Square Support Vector Machine Classifier, is presented. Most of the asthma cases appear during the first years of life. Thus, the early identification of young children being at high risk of developing persistent symptoms of the disease throughout childhood is an important public health priority.Methods. The proposed intelligent system consists of three stages. At the first stage, Principal Component Analysis is used for feature extraction and dimension reduction. At the second stage, the pattern classification is achieved by using Least Square Support Vector Machine Classifier. Finally, at the third stage the performance evaluation of the system is estimated by using classification accuracy and 10-fold cross-validation.Results. The proposed prediction system can be used in asthma outcome prediction with 95.54 % success as shown in the experimental results.Conclusions. This study indicates that the proposed system is a potentially useful decision support tool for predicting asthma outcome and that some risk factors enhance its predictive ability.

scholarly journals Support Vector Machine Performance with Optimal Parameters Identification in Recognising Asphyxiated Infant Cry

2018 ◽  
Vol 7 (3.15) ◽  
pp. 114
Author(s):  
R Sahak ◽  
W Mansor ◽  
Khuan Y. Lee ◽  
A Zabidi
Keyword(s):  
Support Vector Machine ◽  
Optimal Parameter ◽  
Early Stage ◽  
Computation Time ◽  
Principal Component ◽  
Parameters Identification ◽  
Least Square ◽  
Support Vector ◽  
Optimal Parameters ◽  
Infant Cry

Detection of asphyxia in infant at an early stage is crucial to reduce the rate of infant morbidity. The information regarding asphyxia can be extracted from infant cry signals using support vector machine (SVM) combined with effective feature selection methods such as principal component analysis (PCA) or orthogonal least square (OLS). The performance of SVM in recognizing infant cry with asphyxia after undergone comprehensive identification of optimal parameters at the feature extraction and classification stages has not been     reported. This paper describes the two stages of optimal parameter identification; at Mel-frequency Cepstral coefficient (MFCC) analysis stage, SVM with and without employing the PCA and OLS stages, and the performance of the SVM in recognizing infant cry with asphyxia resulted from all levels of optimal parameters identification. The SVM was first optimized after performing MFCC analysis to find the optimum parameters. Two types of kernels were used, the polynomial and RBF kernels. The subsequent SVM optimizations were conducted after PCA and OLS were employed. In the PCA, the significant features were selected using eigenvalue-one-criterion (EOC), cumulative percentage variance (CPV) and the Scree test (SCREE). The SVM performance was evaluated based on classification accuracy and computation time. The experimental results have shown that the optimized SVM was able to recognize the asphyxiated infant cry with an accuracy of 94.84% and computation time of 1.98s using PCA with EOC and RBF kernel.  

scholarly journals Transmission line engineering cost prediction based on principal component analysis and least square support vector machine

2019 ◽  
Vol 136 ◽  
pp. 01028
Author(s):  
Bo Yu ◽  
Zheng Wang ◽  
Xiaomin Liu ◽  
Tong Liu ◽  
Xinyi Liu
Keyword(s):  
Principal Component Analysis ◽  
Support Vector Machine ◽  
Transmission Line ◽  
Principal Component ◽  
Component Analysis ◽  
Least Square ◽  
Small Samples ◽  
Support Vector ◽  
The Cost ◽  
Line Engineering

Due to the many factors affecting the cost of transmission line engineering and the lack of mutual independence, it is difficult to predict the cost. Firstly, the principal component analysis is used to process the original indicator data, eliminating the correlation between the original indicators and extracting the potential comprehensive independent indicators. Then, the new indicator is used as the input set to construct the predictive learning model based on the least squares support vector machine, and the predicted output and the actual value are compared and analyzed. The results show that the model can achieve the desired prediction effect in the case of small samples.

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