IMPLEMENTATION OF SUPPORT VECTOR MACHINES FOR CLASSIFICATION OF CLINICAL DATASETS

2010 ◽  
Vol 07 (04) ◽  
pp. 347-356
Author(s):  
E. SIVASANKAR ◽  
R. S. RAJESH
Keyword(s):  
Feature Extraction ◽  
Dimensional Space ◽  
Principal Component ◽  
Kernel Functions ◽  
Support Vector ◽  
Optimal Hyperplane ◽  
Higher Dimensional ◽  
Vector Machines ◽  
Map Data

In this paper, Principal Component Analysis is used for feature extraction, and a statistical learning based Support Vector Machine is designed for functional classification of clinical data. Appendicitis data collected from BHEL Hospital, Trichy is taken and classified under three classes. Feature extraction transforms the data in the high-dimensional space to a space of fewer dimensions. The classification is done by constructing an optimal hyperplane that separates the members from the nonmembers of the class. For linearly nonseparable data, Kernel functions are used to map data to a higher dimensional space and there the optimal hyperplane is found. This paper works with different SVMs based on radial basis and polynomial kernels, and their performances are compared.

scholarly journals Analyzing the Effectiveness of the Brain–Computer Interface for Task Discerning Based on Machine Learning

Sensors ◽  
2020 ◽  
Vol 20 (8) ◽  
pp. 2403
Author(s):  
Jakub Browarczyk ◽  
Adam Kurowski ◽  
Bozena Kostek
Keyword(s):  
Feature Extraction ◽  
Principal Component ◽  
Component Analysis ◽  
Mental States ◽  
Extraction Methods ◽  
Support Vector ◽  
Discrete Wavelet ◽  
K Nearest Neighbors ◽  
Vector Machines

The aim of the study is to compare electroencephalographic (EEG) signal feature extraction methods in the context of the effectiveness of the classification of brain activities. For classification, electroencephalographic signals were obtained using an EEG device from 17 subjects in three mental states (relaxation, excitation, and solving logical task). Blind source separation employing independent component analysis (ICA) was performed on obtained signals. Welch’s method, autoregressive modeling, and discrete wavelet transform were used for feature extraction. Principal component analysis (PCA) was performed in order to reduce the dimensionality of feature vectors. k-Nearest Neighbors (kNN), Support Vector Machines (SVM), and Neural Networks (NN) were employed for classification. Precision, recall, F1 score, as well as a discussion based on statistical analysis, were shown. The paper also contains code utilized in preprocessing and the main part of experiments.

scholarly journals Superpixel-Based Minimum Noise Fraction Feature Extraction for Classification of Hyperspectral Images

2020 ◽  
Vol 37 (5) ◽  
pp. 812-822
Author(s):  
Behnam Asghari Beirami ◽  
Mehdi Mokhtarzade
Keyword(s):  
Feature Extraction ◽  
Extraction Methods ◽  
Hyperspectral Images ◽  
Support Vector ◽  
Minimum Noise Fraction ◽  
Vector Machines ◽  
Noise Covariance ◽  
Noise Fraction ◽  
Minimum Noise

In this paper, a novel feature extraction technique called SuperMNF is proposed, which is an extension of the minimum noise fraction (MNF) transformation. In SuperMNF, each superpixel has its own transformation matrix and MNF transformation is performed on each superpixel individually. The basic idea behind the SuperMNF is that each superpixel contains its specific signal and noise covariance matrices which are different from the adjacent superpixels. The extracted features, owning spatial-spectral content and provided in the lower dimension, are classified by maximum likelihood classifier and support vector machines. Experiments that are conducted on two real hyperspectral images, named Indian Pines and Pavia University, demonstrate the efficiency of SuperMNF since it yielded more promising results than some other feature extraction methods (MNF, PCA, SuperPCA, KPCA, and MMP).

Classification in GIS Using Support Vector Machines

2010 ◽  
pp. 106-112 ◽  
Author(s):  
Alina Lazar ◽  
Bradley A. Shellito
Keyword(s):  
Support Vector Machines ◽  
Urban Development ◽  
Rural Areas ◽  
Urban Areas ◽  
Kernel Functions ◽  
Support Vector ◽  
Future Trends ◽  
Error Signal ◽  
Vector Machines

Support Vector Machines (SVM) are powerful tools for classification of data. This article describes the functionality of SVM including their design and operation. SVM have been shown to provide high classification accuracies and have good generalization capabilities. SVM can classify linearly separable data as well as nonlinearly separable data through the use of the kernel function. The advantages of using SVM are discussed along with the standard types of kernel functions. Furthermore, the effectiveness of applying SVM to large, spatial datasets derived from Geographic Information Systems (GIS) is also described. Future trends and applications are also discussed – the described extracted dataset contains seven independent variables related to urban development plus a class label which denotes the urban areas versus the rural areas. This large dataset, with over a million instances really proves the generalization capabilities of the SVM methods. Also, the spatial property allows experts to analyze the error signal.

scholarly journals A study on the early detection of colon cancer using the methods of wavelet feature extraction and SVM classifications of FTIR

2008 ◽  
Vol 22 (5) ◽  
pp. 397-404 ◽  
Author(s):  
Cun-Gui Cheng ◽  
Yu-Mei Tian ◽  
Wen-Ying Jin
Keyword(s):  
Colon Cancer ◽  
Feature Extraction ◽  
Early Detection ◽  
Kernel Functions ◽  
Early Carcinoma ◽  
Support Vector ◽  
Advanced Carcinoma ◽  
Sd Rats ◽  
Rbf Kernel

This paper introduces a new method for the early detection of colon cancer using a combination of feature extraction based on wavelets for Fourier Transform Infrared Spectroscopy (FTIR) and classification using the Support Vector Machine (SVM). The FTIR data collected from 36 normal SD rats, 60 1,2-DMH-induced SD rats, and 44 second generation rats of those induced rats was first preprocessed. Then, 12 feature variants were extracted using continuous wavelet analysis. The extracted feature variants were then inputted into the SVM for classification of normal, dysplasia, early carcinoma, and advanced carcinoma. Among the kernel functions the SVM used, the Poly and RBF kernels had the highest accuracy rates. The accuracy of the Poly kernel in normal, dysplasia, early carcinoma, and advanced carcinoma were 100, 97.5, 95% and 100% respectively. The accuracy of RBF kernel in normal, dysplasia, early carcinoma, and advanced carcinoma was 100, 95, 95% and 100% respectively. The results indicated that this method could effectively and easily diagnose colon cancer in its early stages.

Keyboard Characters Recognition with Support Vector Machine

2013 ◽  
Vol 336-338 ◽  
pp. 2283-2287
Author(s):  
Xin Wen Gao ◽  
Xing Jian Guan ◽  
Ben Bo Guan
Keyword(s):  
Support Vector Machine ◽  
Feature Extraction ◽  
Dimensional Space ◽  
Training Data ◽  
Support Vector ◽  
Inspection System ◽  
Higher Dimensional ◽  
Key Technologies ◽  
Recognition Function ◽  
The Difference

This paper proposed a method to detect the defects of keyboard characters. The work, which is a part of the keyboard inspection system, integrates two key technologies to realize the recognition function. First, Feature extraction is applied to select the best properties of the keyboard characters to distinguish the difference and six features are chosen. Second, we integrate support vector machine (SVM) into the classification method and the radial basis kernel function is used to map the training data into higher dimensional space to facilitate the classification. We get a satisfied result in the classification finally which demonstrate the proposed approach is effective.

scholarly journals Algorithms of Hard c-Means Clustering Using Kernel Functions in Support Vector Machines

2003 ◽  
Vol 7 (1) ◽  
pp. 19-24 ◽  
Author(s):  
Sadaaki Miyamoto ◽  
◽  
Youichi Nakayama ◽  
Keyword(s):  
Support Vector Machines ◽  
Dimensional Space ◽  
Iterative Algorithms ◽  
Kernel Functions ◽  
High Dimensional ◽  
Support Vector ◽  
High Dimensional Space ◽  
Numerical Examples ◽  
Vector Machines

We discuss hard c-means clustering using a mapping into a high-dimensional space considered within the theory of support vector machines. Two types of iterative algorithms are developed. Effectiveness of the proposed method is shown by numerical examples.

scholarly journals Comparison some of kernel functions with support vector machines classifier for thalassemia dataset

2021 ◽  
Vol 10 (2) ◽  
pp. 430
Author(s):  
Ilsya Wirasati ◽  
Zuherman Rustam ◽  
Jane Eva Aurelia ◽  
Sri Hartini ◽  
Glori Stephani Saragih
Keyword(s):  
Kernel Functions ◽  
Polynomial Kernel ◽  
Support Vector ◽  
Medical Field ◽  
Treatment Technology ◽  
Blood Disorder ◽  
Vector Machines ◽  
Improve Accuracy ◽  
Rbf Kernel

<span id="docs-internal-guid-9a30056f-7fff-8ff1-59e1-69f89f4280bd"><span>In the medical field, accurate classification of medical data is really important because of its impact on disease detection and patient’s treatment. Technology, machine learning, is needed to help medical staff to improve accuracy to classify disease. This research discussed some kernel functions, such as gaussian radial basis function (RBF) kernel, Polynomial kernel, and linear kernel with support vector machine (SVM) to classify thalassemia data. Thalassemia is a genetic blood disorder which is also one of the major public health problems. In this paper, there is an explanation about thalassemia, SVM, and some of the kernel functions that serve as a comprehensive source for the next research about this topic. Furthermore, there is a comparison result from three kernel functions to find out which one has the best performance. The result is gaussian RBF kernel with SVM is the best method with an average of accuracy 99,63%. </span></span>

scholarly journals Feature extraction from terahertz pulses for classification of RNA data via support vector machines

2006 ◽  
Author(s):  
Xiaoxia Yin ◽  
Brian W.-H. Ng ◽  
Bernd Fischer ◽  
Bradley Ferguson ◽  
Samuel P. Mickan ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Support Vector Machines ◽  
Support Vector ◽  
Terahertz Pulses ◽  
Vector Machines

scholarly journals On the classification of arrhythmia using supplementary features from Tetrolet transforms

2019 ◽  
Vol 9 (6) ◽  
pp. 5006
Author(s):  
G. Jayagopi ◽  
S. Pushpa
Keyword(s):  
Heart Diseases ◽  
Classification Performance ◽  
Kernel Functions ◽  
Support Vector ◽  
Ecg Signals ◽  
The People ◽  
Vector Machines ◽  
Rbf Kernel ◽  
Electrocardiogram Ecg

<span>Heart diseases had been molded as potential threats to human lives, especially to elderly people in recent days due to the dynamically varying food habits among the people. However, these diseases could be easily caught by proper analysis of Electrocardiogram (ECG) signals acquired from individuals. This paper proposes a better method to detect and classify the arrhythmia using 15 features which include 4 R-R interval features, 3 statistical and 6 chaotic features estimated from ECG signals. Additionally, Entropy and Energy features had been gained after converting one dimensional ECG signals to two dimensional data and applied Tetrolet transforms on that.  Total numbers of 15 features had been utilized to classify the heart beats from the benchmark MIT-Arrhythmia database using Support Vector Machines (SVM). The classification performance was analyzed under various kernel functions and different Tetrolet decomposition levels. It is found that Radial Basis Function (RBF) kernel could perform better than linear and polynomial kernels. This research attempt yielded an accuracy of 99.35 % against the existing works. Moreover, addition of two more features had introduced a negligible overhead of time. Hence, this method is better suitable to detect and classify the Arrhythmia in both online and offline.</span>

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