Integration base classifiers based on their decision boundary

Abstract

Multiple classifier systems are used to improve the performance of base classifiers. One of the most important steps in the formation of multiple classifier systems is the integration process in which the base classifiers outputs are combined. The most commonly used classifiers outputs are class labels, the ranking list of possible classes or confidence levels. In this paper, we propose an integration process which takes place in the “geometry space”. It means that we use the decision boundary in the integration process. The results of the experiment based on several data sets show that the proposed integration algorithm is a promising method for the development of multiple classifiers systems.