Homophily of Neighborhood in Graph Relational Classifier
SOFSEM '10 Proceedings of the 36th Conference on Current Trends in Theory and Practice of Computer Science
Semi-supervised tag recommendation - using untagged resources to mitigate cold-start problems
PAKDD'10 Proceedings of the 14th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part I
Time-Evolving relational classification and ensemble methods
PAKDD'12 Proceedings of the 16th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part I
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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).