Relational Ensemble Classification

Christine Preisach, Lars Schmidt-Thieme

Abstract: Relational classification aims at including relations among entities, for example taking relations between documents such as a common author or citations into account. However, considering more than one relation can further improve classification accuracy. In this paper we introduce a new approach to make use of several relations as well as both relations and attributes for classification using ensemble methods. To accomplish this, we present a generic relational ensemble model, that can use different relational and local classifiers as components. Furthermore, we discuss solutions for several problems concerning relational data such as heterogeneity, sparsity, and multiple relations. Finally, we provide empirical evidence, that our relational ensemble methods outperform existing relational classification methods, even rather complex models such as relational probability trees (RPTs), relational dependency networks (RDNs) and relational Bayesian classifiers (RBCs).