Facing the classification of binary problems with a GSA-SVM hybrid system
This paper hybridizes the gravitational search algorithm (GSA) with support vector machine (SVM) and makes a novel GSA-SVM hybrid system to improve classification accuracy with an appropriate feature subset in binary problems. In order to simultaneously optimize the input feature subset selection and the SVM parameter setting, a discrete GSA is combined with a continuous-valued GSA in this system. We evaluate the proposed hybrid system on several UCI machine learning benchmark examples. The results show that the proposed approach is able to select the discriminating input features correctly and achieve high classification accuracy which is comparable to or better than well-known similar classifier systems. (C) 2011 Elsevier Ltd. All rights reserved.
gravitational search algorithm support vector machine binary classification feature selection parameter tuning