Integration of Linear SVM Classifiers in Geometric Space Using the Median

Abstract

An ensemble of classifiers can improve the performance of a pattern recognition system. The task of constructing multiple classifier systems can be generally divided into three steps: generation, selection and integration. In this paper, we propose an integration process which takes place in the geometric space. It means that the fusion of base classifiers is done using decision boundaries. In our approach, we use the linear SVM model as a base classifier, the selection process is based on the accuracy and the final decision boundary is calculated by using the median of the decision boundary. The aim of the experiments was to compare the proposed algorithm and the majority voting method.