The effect of gamma value on support vector machine performance with different kernels

Currently, the support vector machine (SVM) regarded as one of supervised machine learning algorithm that provides analysis of data for classification and regression. This technique is implemented in many fields such as bioinformatics, face recognition, text and hypertext categorization, generalized...

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Published in:International journal of electrical and computer engineering (Malacca, Malacca) Vol. 10; no. 5; p. 5497
Main Authors: Al-Mejibli, Intisar Shadeed, Alwan, Jwan K., Abd, Dhafar Hamed
Format: Journal Article
Language:English
Published: Yogyakarta IAES Institute of Advanced Engineering and Science 01.10.2020
Subjects:
Algorithms
Bioinformatics
Classification
Classifiers
Datasets
Face recognition
Hypertext
Kernel functions
Machine learning
Mathematical analysis
Parameters
Polynomials
Predictive control
Regression analysis
Support vector machines
ISSN:2088-8708, 2722-2578, 2088-8708
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Abstract Currently, the support vector machine (SVM) regarded as one of supervised machine learning algorithm that provides analysis of data for classification and regression. This technique is implemented in many fields such as bioinformatics, face recognition, text and hypertext categorization, generalized predictive control and many other different areas. The performance of SVM is affected by some parameters, which are used in the training phase, and the settings of parameters can have a profound impact on the resulting engine’s implementation. This paper investigated the SVM performance based on value of gamma parameter with used kernels. It studied the impact of gamma value on (SVM) efficiency classifier using different kernels on various datasets descriptions. SVM classifier has been implemented by using Python. The kernel functions that have been investigated are polynomials, radial based function (RBF) and sigmoid. UC irvine machine learning repository is the source of all the used datasets. Generally, the results show uneven effect on the classification accuracy of three kernels on used datasets. The changing of the gamma value taking on consideration the used dataset influences polynomial and sigmoid kernels. While the performance of RBF kernel function is more stable with different values of gamma as its accuracy is slightly changed.
AbstractList Currently, the support vector machine (SVM) regarded as one of supervised machine learning algorithm that provides analysis of data for classification and regression. This technique is implemented in many fields such as bioinformatics, face recognition, text and hypertext categorization, generalized predictive control and many other different areas. The performance of SVM is affected by some parameters, which are used in the training phase, and the settings of parameters can have a profound impact on the resulting engine’s implementation. This paper investigated the SVM performance based on value of gamma parameter with used kernels. It studied the impact of gamma value on (SVM) efficiency classifier using different kernels on various datasets descriptions. SVM classifier has been implemented by using Python. The kernel functions that have been investigated are polynomials, radial based function (RBF) and sigmoid. UC irvine machine learning repository is the source of all the used datasets. Generally, the results show uneven effect on the classification accuracy of three kernels on used datasets. The changing of the gamma value taking on consideration the used dataset influences polynomial and sigmoid kernels. While the performance of RBF kernel function is more stable with different values of gamma as its accuracy is slightly changed.
Author Al-Mejibli, Intisar Shadeed
Abd, Dhafar Hamed
Alwan, Jwan K.
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SubjectTerms Algorithms
Bioinformatics
Classification
Classifiers
Datasets
Face recognition
Hypertext
Kernel functions
Machine learning
Mathematical analysis
Parameters
Polynomials
Predictive control
Regression analysis
Support vector machines
Title The effect of gamma value on support vector machine performance with different kernels
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