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Differential Search Algorithm-based Parametric Optimization of Fuzzy Generalized Eigenvalue Proximal Support Vector Machine

by M. H. Marghny, Rasha M. Abd El-aziz, Ahmed I. Taloba
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 108 - Number 19
Year of Publication: 2014
Authors: M. H. Marghny, Rasha M. Abd El-aziz, Ahmed I. Taloba
10.5120/19023-0540

M. H. Marghny, Rasha M. Abd El-aziz, Ahmed I. Taloba . Differential Search Algorithm-based Parametric Optimization of Fuzzy Generalized Eigenvalue Proximal Support Vector Machine. International Journal of Computer Applications. 108, 19 ( December 2014), 38-46. DOI=10.5120/19023-0540

@article{ 10.5120/19023-0540,
author = { M. H. Marghny, Rasha M. Abd El-aziz, Ahmed I. Taloba },
title = { Differential Search Algorithm-based Parametric Optimization of Fuzzy Generalized Eigenvalue Proximal Support Vector Machine },
journal = { International Journal of Computer Applications },
issue_date = { December 2014 },
volume = { 108 },
number = { 19 },
month = { December },
year = { 2014 },
issn = { 0975-8887 },
pages = { 38-46 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume108/number19/19023-0540/ },
doi = { 10.5120/19023-0540 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:43:26.281201+05:30
%A M. H. Marghny
%A Rasha M. Abd El-aziz
%A Ahmed I. Taloba
%T Differential Search Algorithm-based Parametric Optimization of Fuzzy Generalized Eigenvalue Proximal Support Vector Machine
%J International Journal of Computer Applications
%@ 0975-8887
%V 108
%N 19
%P 38-46
%D 2014
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Support Vector Machine (SVM) is an effective model for many classification problems. However, SVM needs the solution of a quadratic program which require specialized code. In addition, SVM has many parameters, which affects the performance of SVM classi?er. Recently, the Generalized Eigenvalue Proximal SVM (GEPSVM) has been presented to solve the SVM complexity. In real world applications data may affected by error or noise, working with this data is a challenging problem. In this paper, an approach has been proposed to overcome this problem. This method is called DSA-GEPSVM. The main improvements are carried out based on the following: 1) a novel fuzzy values in the linear case. 2) A new Kernel function in the nonlinear case. 3) Differential Search Algorithm (DSA) is reformulated to ?nd near optimal values of the GEPSVM parameters and its kernel parameters. The experimental results show that the proposed approach is able to find the suitable parameter values, and has higher classification accuracy compared with some other algorithms.

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Index Terms

Computer Science
Information Sciences

Keywords

Support Vector Machines Generalized Eigenvalues Proximal Classifier Fuzzy Data Classification Differential Search Algorithm Kernel Function.

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