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  • 标题:SS-SVM (3SVM): A New Classification Method for Hepatitis Disease Diagnosis
  • 本地全文:下载
  • 作者:Mohammed H. Afif ; Abdel-Rahman Hedar ; Taysir H. Abdel Hamid
  • 期刊名称:International Journal of Advanced Computer Science and Applications(IJACSA)
  • 印刷版ISSN:2158-107X
  • 电子版ISSN:2156-5570
  • 出版年度:2013
  • 卷号:4
  • 期号:2
  • DOI:10.14569/IJACSA.2013.040208
  • 出版社:Science and Information Society (SAI)
  • 摘要: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%.
  • 关键词:thesai; IJACSA; thesai.org; journal; IJACSA papers; Support Vector Machine; Scatter Search; Classification; Parameter tuning
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