LEAST SQUARES SUPPORT VECTOR MACHINES PARAMETER OPTIMIZATION BASED ON IMPROVED ANT COLONY ALGORITHM FOR HEPATITIS DIAGNOSIS
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.
Keywords: Classification,
Least Squares Support Vector Machines, Improved Ant Colony Algorithm, Local
Fisher Discriminant Analysis, Hepatitis Disease
Author: Nursuci Putri Husain,
Nursanti Novi Arisa, Putri Nur Rahayu, Agus Zainal Arifin, Darlis Herumurti
Journal Code: jptkomputergg170008
