The use of geometric mean in the process of integration of three base classifiers

Abstract

One of the most important steps in the formation of multiple classifier systems is the integration process also called the base classifiers fusion. The fusion process may be applied either to class labels or confidence levels (discriminant functions). These are the two main methods for combining base classifiers. In this paper, we propose an integration process which takes place in the geometry space. It means that the fusion of base classifiers is done using decision boundaries. In our approach, the final decision boundary is calculated by using the geometric mean. The algorithm presented in the paper concerns the case of 3 basic classifiers and two-dimensional features space. 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.