Efficient Binary Classifier for Prediction of Diabetes Using Data Preprocessing and Support Vector Machine

2015 ◽  
pp. 131-140
Author(s):  
Madhavi Pradhan ◽  
G. R. Bamnote
Keyword(s):  
Support Vector Machine ◽  
Data Preprocessing ◽  
Support Vector ◽  
Binary Classifier ◽  
Using Data ◽  
Prediction Of Diabetes

scholarly journals Application of support vector machine in a traffic lights control

2020 ◽  
Vol 14 ◽  
pp. 37-42
Author(s):  
Artur Całuch ◽  
Adam Cieślikowski ◽  
Małgorzata Plechawska-Wójcik
Keyword(s):  
Support Vector Machine ◽  
Cycle Length ◽  
Support Vector ◽  
Light Cycle ◽  
Traffic Light ◽  
Traffic Lights ◽  
Using Data

This article presents the process of adapting support vector machine model’s parameters used for studying the effect of traffic light cycle length parameter’s value on traffic quality. The survey is carried out using data collected during running simulations in author’s traffic simulator. The article shows results of searching for optimum traffic light cycle length parameter’s value.

Sparse and heuristic support vector machine for binary classifier and regressor fusion

2019 ◽  
Vol 10 (12) ◽  
pp. 3667-3686 ◽  
Author(s):  
Jinhong Huang ◽  
Zhu Liang Yu ◽  
Zhenghui Gu ◽  
Jun Zhang ◽  
Ling Cen
Keyword(s):  
Support Vector Machine ◽  
Support Vector ◽  
Binary Classifier

scholarly journals Classification of Hospital Web Security Efficiency Using Data Envelopment Analysis and Support Vector Machine

2013 ◽  
Vol 2013 ◽  
pp. 1-8 ◽  
Author(s):  
Han-Ying Kao ◽  
Tao-Ku Chang ◽  
Yi-Cheng Chang
Keyword(s):  
Support Vector Machine ◽  
Data Envelopment Analysis ◽  
Web Security ◽  
Support Vector ◽  
Data Envelopment ◽  
Learning Patterns ◽  
Dea Models ◽  
Efficiency Estimation ◽  
Hybrid Data ◽  
Using Data

This study proposes the hybrid data envelopment analysis (DEA) and support vector machine (SVM) approaches for efficiency estimation and classification in web security. In the proposed framework, the factors and efficiency scores from DEA models are integrated with SVM for learning patterns of web security performance and provide further decision support. The numerical case study of hospital web security efficiency is demonstrated to support the feasibility of this design.

scholarly journals DIAGNOSA KERUSAKAN BANTALAN BOLA MENGGUNAKAN METODE SUPPORT VECTOR MACHINE

2019 ◽  
Vol 18 (1) ◽  
Author(s):  
Muhammad Fathurrohman ◽  
R. Lulus Lambang G. H ◽  
Didik Djoko Susilo
Keyword(s):  
Support Vector Machine ◽  
Ball Bearing ◽  
Training Model ◽  
Support Vector ◽  
Classification Problems ◽  
Vibration Signals ◽  
Svm Classification ◽  
Outer Race ◽  
Rbf Kernel ◽  
Using Data

<p><em>Bearings are the critical part of any rotating machine. The catastrophic failure of the bearing can lead to fatal and harmful to the operation of the machine. Therefore, predictive maintenance based on condition monitoring of bearing is very important. The objective of this research is to apply Support Vector Machine (SVM) method for fault diagnosis of the ball bearing. The research was carried out at the bearing test rig. Four types of ball bearing condition, such as normal, inner race defect, ball defect, and outer race defect were measured of the vibration signals using data acquisition with a sampling frequency of 20 kHz at the constant speed of 1400 RPM. Various features were extracted from vibration signals in time domain, such as RMS, variance, standard deviation, crest factor, shape factor, skewness, kurtosis, log energy entropy and sure entropy. PCA transformation was employed to reduce the dimension of feature extracted data. SVM classification problems were solved using MATLAB 2016a. The results showed that the application of RBF kernel function with the C parameter =1 was the best configuration. The training model accuracy was 98.93% and the testing accuracy of SVM was 97.5%. Finally, the research results show that the SVM classification method can be used to diagnose the fault condition of the ball bearing.</em><em>.</em></p>

A Method for Classification Using Data Mining Technique for Diabetes

2016 ◽  
pp. 738-761
Author(s):  
Ahmad Al-Khasawneh
Keyword(s):  
Data Mining ◽  
Support Vector Machine ◽  
Classification Accuracy ◽  
Health Information System ◽  
Parameters Optimization ◽  
Support Vector ◽  
Data Mining Algorithms ◽  
Predictive Data Mining ◽  
Severity Of The Disease ◽  
Using Data

Many researchers in the health information system field have been attracted to develop computer applications that help in the diagnosis process. Imperatively, data mining algorithms address the vital role in all of these applications. Many contributions were made in this area. There has always been a debate on the algorithm that gives the best classifier, the parameters to be used, the dataset pre-processing steps, etc. In this paper, the author largely emphasizes that the best way to build a predictive model with relatively high classification accuracy is to build several predictive models and to choose the model that gives the best results through parameters optimization. Diagnosing diabetes mellitus has gained considerable attention in the last few decades due to the increased severity of the disease. In this research, the author reviews four predictive data mining approaches that are being used in diagnosing diabetes. Four models were implemented to diagnose diabetes from PIMA dataset; k-nearest neighbour, support vector machine, multilayer perceptron neural network, and naive bayesian network. Giving the highest classification accuracy, support vector machine technique outperformed the others with a value of 78.83%.

Nonlinearity Compensation for Thermocouple Vacuum Gauge Using Particle Swarm Optimized Least Squares Support Vector Machine

2013 ◽  
Vol 321-324 ◽  
pp. 2177-2182
Author(s):  
Yao Geng Tang ◽  
Song Gao ◽  
Xing Qu
Keyword(s):  
Support Vector Machine ◽  
Least Squares ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Support Vector ◽  
Vacuum Gauge ◽  
Swarm Optimization ◽  
Svm Model ◽  
Using Data ◽  
Nonlinearity Characteristic

A method for compensating nonlinear characteristic of thermocouple vacuum gauge is proposed. Least squares support vector machine (LS-SVM) is adopt as compensation model, of which parameters are optimized using particle swarm optimization (PSO) algorithm. Experimental results using data obtained on-site show that the proposed approach effectively compensates the nonlinearity characteristic, and the accuracy of this method is higher than those obtained by SVM model.

scholarly journals Nonlinear Multivariate Calibration of Shelf Life of Preserved Eggs (Pidan) by Near Infrared Spectroscopy: Stacked Least Squares Support Vector Machine with Ensemble Preprocessing

2013 ◽  
Vol 2013 ◽  
pp. 1-7 ◽  
Author(s):  
Lu Xu ◽  
Si-Min Yan ◽  
Chen-Bo Cai ◽  
Xiao-Ping Yu ◽  
Jian-Hui Jiang ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Shelf Life ◽  
Least Squares ◽  
Nonlinear Model ◽  
Near Infrared ◽  
Multivariate Calibration ◽  
Data Preprocessing ◽  
Computational Time ◽  
Support Vector ◽  
Calibration Model

This paper aims at developing a rapid and nondestructive method for analyzing the shelf life of preserved eggs (pidan) by near infrared (NIR) spectroscopy and nonlinear multivariate calibration. A major concern with a nonlinear model is that the noncomposition-correlated spectral variations among pidan objects of different batches and production dates would unnecessarily increase model complexity and cause overfitting and degradation of prediction. To reduce the negative influence of unwanted spectral variations, stacked least squares support vector machine (LS-SVM) with an ensemble of 62 commonly used preprocessing methods is proposed to automatically optimize data preprocessing and develop the nonlinear model. The analysis results indicate that stacked LS-SVM can obtain stable calibration model, and the prediction accuracy is improved compared with models with single-preprocessing methods. Since LS-SVM is much faster than its ordinary counterparts, stacked LS-SVM with ensemble preprocessing can be performed within an acceptable computational time. When the objects and spectral variations are very complex, the proposed method can provide a useful tool for data preprocessing and nonlinear multivariate calibration.

scholarly journals PMSVM: An Optimized Support Vector Machine Classification Algorithm Based on PCA and Multilevel Grid Search Methods

2015 ◽  
Vol 2015 ◽  
pp. 1-15 ◽  
Author(s):  
Yukai Yao ◽  
Hongmei Cui ◽  
Yang Liu ◽  
Longjie Li ◽  
Long Zhang ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Roc Curve ◽  
Support Vector Machine Classifier ◽  
Data Preprocessing ◽  
Parameters Optimization ◽  
Experimental Results ◽  
Support Vector ◽  
Grid Search ◽  
Search Methods ◽  
Sensitivity Specificity

We propose an optimized Support Vector Machine classifier, named PMSVM, in which System Normalization, PCA, and Multilevel Grid Search methods are comprehensively considered for data preprocessing and parameters optimization, respectively. The main goals of this study are to improve the classification efficiency and accuracy of SVM. Sensitivity, Specificity, Precision, and ROC curve, and so forth, are adopted to appraise the performances of PMSVM. Experimental results show that PMSVM has relatively better accuracy and remarkable higher efficiency compared with traditional SVM algorithms.

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