Classifier Ensembles for Vector Space Embedding of Graphs

Abstract

Classifier ensembles aim at a more accurate classification than single classifiers. Different approaches to building classifier ensembles have been proposed in the statistical pattern recognition literature. However, in structural pattern recognition, classifier ensembles have been rarely used. In this paper we introduce a general methodology for creating structural classifier ensembles. Our representation formalism is based on graphs and includes strings and trees as special cases. In the proposed approach we make use of graph embedding in real vector spaces by means of prototype selection. Since we use randomized prototype selection, it is possible to generate n different vector sets out of the same underlying graph set. Thus, one can train an individual base classifier for each vector set und combine the results of the classifiers in an appropriate way. We use extended support vector machines for classification and combine them by means of three different methods. In experiments on semi-artificial and real data we show that it is possible to outperform the classification accuracy obtained by single classifier systems in the original graph domain as well as in the embedding vector spaces.