GA_SVM

2017 ◽  
pp. 359-397 ◽  
Author(s):  
Dilip Kumar Choubey ◽  
Sanchita Paul
Keyword(s):  
Diagnostic System ◽  
Computation Time ◽  
Modern Society ◽  
Attribute Selection ◽  
Polynomial Kernel ◽  
Sigmoid Function ◽  
Linear Kernel ◽  
Rbf Kernel ◽  
Life Threatening ◽  
Kernel Svm

The modern society is prone to many life-threatening diseases which if diagnosis early can be easily controlled. The implementation of a disease diagnostic system has gained popularity over the years. The main aim of this research is to provide a better diagnosis of diabetes. There are already several existing methods, which have been implemented for the diagnosis of diabetes. In this manuscript, firstly, Polynomial Kernel, RBF Kernel, Sigmoid Function Kernel, Linear Kernel SVM used for the classification of PIDD. Secondly GA used as an Attribute selection method and then used Polynomial Kernel, RBF Kernel, Sigmoid Function Kernel, Linear Kernel SVM on that selected attributes of PIDD for classification. So, here compared the results with and without GA in PIDD, and Linear Kernel proved better among all of the noted above classification methods. It directly seems in the paper that GA is removing insignificant features, reducing the cost and computation time and improving the accuracy, ROC of classification. The proposed method can be also used for other kinds of medical diseases.

Classification of Diabetes by Kernel based SVM with PSO

2019 ◽  
Vol 12 ◽  
Author(s):  
Dilip Kumar Choubey ◽  
Sudhakar Tripathi ◽  
Prabhat Kumar ◽  
Vaibhav Shukla ◽  
Vinay Kumar Dhandhania
Keyword(s):  
Computation Time ◽  
Research Paper ◽  
Disease Diagnosis ◽  
Feature Reduction ◽  
Classification Method ◽  
Polynomial Kernel ◽  
Second Phase ◽  
Sigmoid Function ◽  
Classification Methods ◽  
Kernel Svm

Background: Classification method is needed to deduce the possible errors and assist the doctor’s. These methods are used in every many of our lives to take suitable decisions. It is well known that classification is an efficient, effective and broadly utilized strategy in several applications such as medical disease diagnosis, etc. The prime objective of this research paper is to achieve an efficient and effective classification method for Diabetes. Discussion: The proposed methodology comprises of two phases: The first phase deals with description of Pima Indian Diabetes Dataset and Localized Diabetes Dataset whereas in the second phase dataset has been processed through two different approaches. First approach entails classification through Polynomial Kernel, RBF Kernel, Sigmoid Function Kernel and Linear Kernel SVM on Pima Indian Diabetes Dataset and Localized Diabetes Dataset. In the second approach, PSO have been utilized as a feature reduction method followed by using the same set of classification methods used in the first approach. PSO_Linear Kernel SVM provides the highest accuracy and ROC for both the above mentioned dataset. Conclusion: In this research paper, comparative analysis of outcomes w.r.t. performance assessment has been done using both with PSO and without PSO for the same set of classification methods. Finally, it has been concluded that PSO is selecting the relevant features, reducing the expense and computation time while improving the ROC and accuracy. The used methodology may similarly be implemented in other medical diseases.

Rank Predictions of Internal Corrosion of Gathering Pipelines in a Natural Gas Field With a Multi-Kernel SVM Method

2020 ◽  
Author(s):  
Yuejiao Li ◽  
Weiguo Zeng ◽  
Xiufeng Li ◽  
Fajun Ren ◽  
Haijun Hu
Keyword(s):  
Natural Gas ◽  
Gaussian Kernel ◽  
Polynomial Kernel ◽  
Linear Kernel ◽  
Gas Field ◽  
Internal Corrosion ◽  
H2s Corrosion ◽  
Corrosion Risk ◽  
Internal Co2 ◽  
Kernel Svm

Abstract Internal CO2/H2S corrosion of gathering pipelines is a serious problem in natural gas plant. It is important for field engineers to assess the corrosion degree and control corrosion risk. A multi-kernel support-vector-machine (SVM) method is presented to rank internal corrosion of gathering pipelines according to the NACE RP-0775-91 standard. By considering the nonlinear indivisibility between data, we combined three kinds of kernels (linear kernel, polynomial kernel, and Gaussian kernel) into a multi-kernel SVM to rank the internal CO2/H2S corrosion of gathering pipelines. The method was applied to a natural gas field in northwest China. Corrosion data were collected and analyzed. The prediction accuracy of the multi-kernel SVM method for ranking CO2/H2S corrosion was 66%, which is higher than the results of the single-kernel SVM methods (linear kernel, polynomial kernel and Gaussian kernel), whose prediction accuracies are 50%, 48% and 54% respectively. These findings could help field engineers rank corrosion and reduce the corrosion risk.

Computer aided system for breast cancer in digitized mammogram using shearlet band features with LS-SVM classifier

2016 ◽  
Vol 14 (03) ◽  
pp. 1650017 ◽  
Author(s):  
M. Kanchana ◽  
P. Varalakshmi
Keyword(s):  
Breast Cancer ◽  
Kernel Functions ◽  
Polynomial Kernel ◽  
Svm Classifier ◽  
Operating Characteristics ◽  
Directional Information ◽  
Rbf Kernel ◽  
Analysis Society ◽  
Life Threatening ◽  
Leave One Out

Breast cancer is life threatening and dangerous diseases among the women across the world. In this paper, mammogram image classification performed using LS-SVM with various kernels functions namely, Gaussian Radial Basis Function (GRBF) kernel, Polynomial kernel, Quadratic kernel, Linear kernel and MLP kernel. Shearlet transform is a multidimensional version of the composite dilation wavelet transform, and is especially designed to address anisotropic and directional information at various scales and directions, which is used to decompose the regions of interest (ROI) image after preprocessing stage. Initially, mammogram images are transformed into different resolution levels from 2 levels to 4 levels with various directions varying from 2 to 64. The evaluation of the system is carried out on the Mammography Image Analysis Society (MIAS) database. From the experimental analysis, based on classification accuracy and Receiver Operating Characteristics (ROC), it is concluded that LS-SVM with Gaussian RBF kernel function outperforms than Quadratic, polynomial, linear and MLP kernel functions. The classifiers were validated with leave-one-out (training) and cross-validation (testing) modes.

scholarly journals Diagnosis and Prediction of Large-For-Gestational-Age Fetus Using the Stacked GeneralizationMethod

2019 ◽  
Vol 9 (20) ◽  
pp. 4317 ◽  
Author(s):  
Akhtar ◽  
Li ◽  
Pei ◽  
Imran ◽  
Rajput ◽  
...  
Keyword(s):  
Gestational Age ◽  
Classification Scheme ◽  
Feature Vector ◽  
Information Gain ◽  
Diagnostic System ◽  
Large For Gestational Age ◽  
Linear Kernel ◽  
Recall Accuracy ◽  
Rbf Kernel ◽  
Low Performance

An accurate and efficient Large-for-Gestational-Age (LGA) classification system isdeveloped to classify a fetus as LGA or non-LGA, which has the potential to assist paediatricians andexperts in establishing a state-of-the-art LGA prognosis process. The performance of the proposedscheme is validated by using LGA dataset collected from the National Pre-Pregnancy and ExaminationProgram of China (2010–2013). A master feature vector is created to establish primarily datapre-processing, which includes a features’ discretization process and the entertainment of missingvalues and data imbalance issues. A principal feature vector is formed using GridSearch-basedRecursive Feature Elimination with Cross-Validation (RFECV) + Information Gain (IG) featureselection scheme followed by stacking to select, rank, and extract significant features from the LGAdataset. Based on the proposed scheme, different features subset are identified and provided tofour different machine learning (ML) classifiers. The proposed GridSearch-based RFECV+IG featureselection scheme with stacking using SVM (linear kernel) best suits the said classification processfollowed by SVM (RBF kernel) and LR classifiers. The Decision Tree (DT) classifier is not suggestedbecause of its low performance. The highest prediction precision, recall, accuracy, Area Underthe Curve (AUC), specificity, and F1 scores of 0.92, 0.87, 0.92, 0.95, 0.95, and 0.89 are achievedwith SVM (linear kernel) classifier using top ten principal features subset, which is, in fact higherthan the baselines methods. Moreover, almost every classification scheme best performed with tenprincipal feature subsets. Therefore, the proposed scheme has the potential to establish an efficientLGA prognosis process using gestational parameters, which can assist paediatricians and experts toimprove the health of a newborn using computer aided-diagnostic system.

scholarly journals Linear Kernel with Weighted Least Square Regression Co-efficient for SVM Based Tamil Writer Identification

2019 ◽  
Vol 8 (2) ◽  
pp. 586-591
Keyword(s):  
Estimation Method ◽  
Least Square ◽  
The Other ◽  
Writer Identification ◽  
Linear Kernel ◽  
Weighted Least Square ◽  
Linear Polynomial ◽  
Rbf Kernel ◽  
Text Images ◽  
Kernel Svm

Tamil writer identification is the task of identifying writer based on their Tamil handwriting. Our earlier work of this research based on SVM implementation with linear, polynomial and RBF kernel showed that linear kernel attains very low accuracy compared to other two kernels. But the observation shows that linear kernel performs faster than the other kernels and also it shows very less computational complexity. Hence, a modified linear kernel is proposed to enrich the performance of the linear kernel in recognizing the Tamil writer. Weighted least square parameter estimation method is used to estimate the weights for the dot products of the linear kernel. SVM implementation with modified linear kernel is carried out on different text images of handwriting at character, word and paragraph levels. Comparing the performance with linear kernel, the modified kernel with weighted least square parameter reported promising results.

Evaluate the Performance of SVM Kernel Functions for Multiclass Cancer Classification

2020 ◽  
Vol 1 (1) ◽  
pp. 37-41
Author(s):  
Noramalina Mohd Hatta ◽  
Zuraini Ali Shah ◽  
Shahreen Kasim
Keyword(s):  
Kernel Function ◽  
Supervised Classification ◽  
Kernel Functions ◽  
Cancer Classification ◽  
Polynomial Kernel ◽  
Support Vector ◽  
Linear Kernel ◽  
Straight Line ◽  
Rbf Kernel ◽  
Rbf Kernel Function

Multiclass cancer classification is basically one of the challenging fields in machine learning which a fast growing technology that use human behaviour as examples. Supervised classification such Support Vector Machine (SVM) has been used to classify the dataset on classification by its own function and merely known as kernel function. Kernel function has stated to have a problem especially in selecting their best kernels based on a specific datasets and tasks. Besides, there is an issue stated that the kernels function have a high impossibility to distribute the data in straight line. Here, three basic kernel functions was used and tested with selected dataset and they are linear kernel, polynomial kernel and Radial Basis Function (RBF) kernel function. The three kernels were tested by different dataset to gain the accuracy. For a comparison, this study conducting a test by with and without feature selection in SVM classification kernel function since both tests will give different result and thus give a big meaning to the study.

scholarly journals A COMPARISON STUDY OF DIFFERENT KERNEL FUNCTIONS FOR SVM-BASED CLASSIFICATION OF MULTI-TEMPORAL POLARIMETRY SAR DATA

2014 ◽  
Vol XL-2/W3 ◽  
pp. 281-285 ◽  
Author(s):  
B. Yekkehkhany ◽  
A. Safari ◽  
S. Homayouni ◽  
M. Hasanlou
Keyword(s):  
Kernel Function ◽  
Temporal Integration ◽  
Experimental Tests ◽  
Kernel Functions ◽  
Polynomial Kernel ◽  
Support Vector ◽  
Svm Classifier ◽  
Rbf Kernel ◽  
Multi Temporal ◽  
Dimension Space

In this paper, a framework is developed based on Support Vector Machines (SVM) for crop classification using polarimetric features extracted from multi-temporal Synthetic Aperture Radar (SAR) imageries. The multi-temporal integration of data not only improves the overall retrieval accuracy but also provides more reliable estimates with respect to single-date data. Several kernel functions are employed and compared in this study for mapping the input space to higher Hilbert dimension space. These kernel functions include linear, polynomials and Radial Based Function (RBF). <br><br> The method is applied to several UAVSAR L-band SAR images acquired over an agricultural area near Winnipeg, Manitoba, Canada. In this research, the temporal alpha features of H/A/α decomposition method are used in classification. The experimental tests show an SVM classifier with RBF kernel for three dates of data increases the Overall Accuracy (OA) to up to 3% in comparison to using linear kernel function, and up to 1% in comparison to a 3rd degree polynomial kernel function.

scholarly journals PREDICTION OF OZONE (O3) VALUES USING SUPPORT VECTOR REGRESSION METHOD

2021 ◽  
Vol 7 (4) ◽  
pp. 81-88
Author(s):  
Chasandra Puspitasari ◽  
Nur Rokhman ◽  
Wahyono
Keyword(s):  
Air Quality ◽  
Support Vector Regression ◽  
Time Series Data ◽  
Motor Vehicles ◽  
Primary Data ◽  
Series Data ◽  
Polynomial Kernel ◽  
Support Vector ◽  
Linear Kernel ◽  
Air Quality Prediction

A large number of motor vehicles that cause congestion is a major factor in the poor air quality in big cities. Ozone (O3) is one of the main indicators in measuring the level of air pollution in the city of Surabaya to find out how air quality. Prediction of Ozone (O3) value is important as a support for the community and government in efforts to improve the air quality. This study aims to predict the value of Ozone (O3) in the form of time series data using the Support Vector Regression (SVR) method with the Linear, Polynomial, RBF, and ANOVA kernels. The data used in this study are 549 primary data from the daily average of ozone (O3) value of Surabaya in the period 1 July 2017 - 31 December 2018. The data will be used in the training and testing process until prediction results are obtained. The results obtained from this study are the Linear kernel produces the best prediction model with a MAPE value of 21.78% with a parameter value 𝜆 = 0.3; 𝜀 = 0.00001; cLR = 0.005; and C = 0.5. The results of the Polynomial kernel are not much different from the Linear kernel which has a MAPE value of 21.83%. While the RBF and ANOVA kernels each produce a model with MAPE value of 24.49% and 22.0%. These results indicate that the SVR method with the kernels used can predict Ozone values quite well.

Research on an ECG Wireless Monitoring System Based on GSM

2010 ◽  
Vol 43 ◽  
pp. 333-337
Author(s):  
Hong Jin Wang ◽  
Zhen Guang Hu ◽  
Zha Nao Wu
Keyword(s):  
Mobile Phones ◽  
Data Transmission ◽  
Short Range ◽  
Diagnostic System ◽  
Data Monitoring ◽  
Range Data ◽  
Short Message ◽  
Wireless Monitoring ◽  
Life Threatening ◽  
Gsm Network

In order to monitor the heart rate changes of patients with cardiovascular disease, a wireless ECG system based on the GSM network is developed in this paper. The data captured by cardiotachometer will be transmitted to mobile phones via short-range data transmission, to HR data monitoring center via short message of GSM. In which, the expert diagnostic system analyzes those data and put the results back to the user, especially those cases such as instantaneous tachycardia and cardiac arrest. This may be avoid some life-threatening events occurrence.

scholarly journals Detection of Class Regularity with Support Vector Machine methods

2020 ◽  
Vol 11 (1) ◽  
pp. 20
Author(s):  
Ni Wayan Emmy Rosiana Dewi ◽  
I Gede Aris Gunadi ◽  
Gede Indrawan
Keyword(s):  
Classroom Environment ◽  
Video Recording ◽  
Image Data ◽  
Polynomial Kernel ◽  
Support Vector ◽  
Linear Kernel ◽  
Class Noise ◽  
Good Classification ◽  
Irregular Data ◽  
Sound Features

One of the most factor that affects the achievement and learning motivation of students is a conducive classroom environment. It can be seen from the student's regularity in the class. Teachers can determine whether the class is adequate or not by monitoring the class condition through video. The research tries to apply the extraction of imagery and sound features by using the Centroid extraction method and the MFCC along with classifying the regular or irregular classrooms with the SVM methods which are taken by video installed in a classroom. The video will be split into image data and sound data. The process of image data starts with reading the input, then it goes to the stages of preprocessing, segmentation with K-Means, morphology, and the most important part is to get information before it is classified by the SVM method to get its class regularity. The sound frequency will be extracted by the MFCC method and then it is classified by the SVM method to get the class noise. The results of this research get an accuracy value of 78% in the linear kernel and 70% in the polynomial kernel. This research uses 50 test data consisting of 25 regular data and 25 irregular data taken directly through video recording. These results prove that the SVM method has given good classification results for regular and irregular classes.

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