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Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.
SS-SVM (3SVM): A New Classification Method for Hepatitis Disease Diagnosis
Digital Object Identifier (DOI) : 10.14569/IJACSA.2013.040208
Article Published in International Journal of Advanced Computer Science and Applications(IJACSA), Volume 4 Issue 2, 2013.
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%.
Keywords: Support Vector Machine; Scatter Search; Classification; Parameter tuning
Mohammed H. Afif, Abdel-Rahman Hedar, Taysir H. Abdel Hamid and Yousef B. Mahdy, “SS-SVM (3SVM): A New Classification Method for Hepatitis Disease Diagnosis” International Journal of Advanced Computer Science and Applications(IJACSA), 4(2), 2013. http://dx.doi.org/10.14569/IJACSA.2013.040208
@article{Afif2013,
title = {SS-SVM (3SVM): A New Classification Method for Hepatitis Disease Diagnosis},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2013.040208},
url = {http://dx.doi.org/10.14569/IJACSA.2013.040208},
year = {2013},
publisher = {The Science and Information Organization},
volume = {4},
number = {2},
author = {Mohammed H. Afif and Abdel-Rahman Hedar and Taysir H. Abdel Hamid and Yousef B. Mahdy}
}
