LEAST SQUARES SUPPORT VECTOR MACHINES PARAMETER OPTIMIZATION BASED ON IMPROVED ANT COLONY ALGORITHM FOR HEPATITIS DIAGNOSIS

  • Nursuci Putri Husain Department of Informatics, Faculty of Information Technology, Institut Teknologi Sepuluh Nopember (ITS)
  • Nursanti Novi Arisa Department of Informatics, Faculty of Information Technology, Institut Teknologi Sepuluh Nopember (ITS)
  • Putri Nur Rahayu Department of Informatics, Faculty of Information Technology, Institut Teknologi Sepuluh Nopember (ITS)
  • Agus Zainal Arifin Department of Informatics, Faculty of Information Technology, Institut Teknologi Sepuluh Nopember (ITS)
  • Darlis Herumurti Department of Informatics, Faculty of Information Technology, Institut Teknologi Sepuluh Nopember (ITS)
Keywords: Classification, Least Squares Support Vector Machines, Improved Ant Colony Algorithm, Local Fisher Discriminant Analysis, Hepatitis Disease

Abstract

Many kinds of classification method are able to diagnose a patient who suffered Hepatitis disease. One of classification methods that can be used was Least Squares Support Vector Machines (LSSVM). There are two parameters that very influence to improve the classification accuracy on LSSVM, they are kernel parameter and regularization parameter. Determining the optimal parameters must be considered to obtain a high classification accuracy on LSSVM. This paper proposed an optimization method based on Improved Ant Colony Algorithm (IACA) in determining the optimal parameters of LSSVM for diagnosing Hepatitis disease. IACA create a storage solution to keep the whole route of the ants. The solutions that have been stored were the value of the parameter LSSVM. There are three main stages in this study. Firstly, the dimension of Hepatitis dataset will be reduced by Local Fisher Discriminant Analysis (LFDA). Secondly, search the optimal parameter LSSVM with IACA optimization using the data training, And the last, classify the data testing using optimal parameters of LSSVM. Experimental results have demonstrated that the proposed method produces high accuracy value (93.7%) for  the 80-20% training-testing partition.

Published
2017-02-28