In this paper, a new classification approach combining support vector machine with scatter search approach for hepatitis disease diagnosis is presented, called 3SVM. The scatter search approach is used to find near optimal values of SVM parameters and its kernel parameters. The hepatitis dataset is obtained from UCI. Experimental results and comparisons prove that the 3SVM gives better outcomes and has a competitive performance relative to other published methods found in literature, where the average accuracy rate obtained is 98.75\%.
%0 Journal Article
%1 IJACSA.2013.040208
%A Mohammed H. Afif Abdel-Rahman Hedar, Taysir H Abdel Hamid Yousef B Mahdy
%D 2013
%J International Journal of Advanced Computer Science and Applications(IJACSA)
%K Classification; Machine; Parameter Scatter Search; Support Vector tuning
%N 2
%T SS-SVM (3SVM): A New Classification Method for Hepatitis Disease Diagnosis
%U http://ijacsa.thesai.org/
%V 4
%X In this paper, a new classification approach combining support vector machine with scatter search approach for hepatitis disease diagnosis is presented, called 3SVM. The scatter search approach is used to find near optimal values of SVM parameters and its kernel parameters. The hepatitis dataset is obtained from UCI. Experimental results and comparisons prove that the 3SVM gives better outcomes and has a competitive performance relative to other published methods found in literature, where the average accuracy rate obtained is 98.75\%.
@article{IJACSA.2013.040208,
abstract = {In this paper, a new classification approach combining support vector machine with scatter search approach for hepatitis disease diagnosis is presented, called 3SVM. The scatter search approach is used to find near optimal values of SVM parameters and its kernel parameters. The hepatitis dataset is obtained from UCI. Experimental results and comparisons prove that the 3SVM gives better outcomes and has a competitive performance relative to other published methods found in literature, where the average accuracy rate obtained is 98.75\%.},
added-at = {2014-02-21T08:00:08.000+0100},
author = {{Mohammed H. Afif Abdel-Rahman Hedar}, Taysir H Abdel Hamid Yousef B Mahdy},
biburl = {https://www.bibsonomy.org/bibtex/275ae3aa54ea11edb2c99bc75925cccc8/thesaiorg},
interhash = {34067d6cd94627105c9e57d04fbde024},
intrahash = {75ae3aa54ea11edb2c99bc75925cccc8},
journal = {International Journal of Advanced Computer Science and Applications(IJACSA)},
keywords = {Classification; Machine; Parameter Scatter Search; Support Vector tuning},
number = 2,
timestamp = {2014-02-21T08:00:08.000+0100},
title = {{SS-SVM (3SVM): A New Classification Method for Hepatitis Disease Diagnosis}},
url = {http://ijacsa.thesai.org/},
volume = 4,
year = 2013
}