Across-Model Collective Ensemble Classification

Ensemble classification methods that independently construct component models (e. g. , bagging) improve accuracy over single models by reducing the error due to variance. Some work has been done to extend ensemble techniques for classification in relational domains by taking relational data characteristics or multiple link types into account during model construction. However, since these approaches follow the conventional approach to ensemble learning, they improve performance by reducing the error due to variance in learning. We note however, that variance in inference can be an additional source of error in relational methods that use collective classification, since inferred values are propagated during inference. We propose a novel ensemble mechanism for collective classification that reduces both learning and inference variance, by incorporating prediction averaging into the collective inference process itself. We show that our proposed method significantly outperforms a straightforward relational ensemble baseline on both synthetic and real-world datasets.

Authors

• Hoda Eldardiry Purdue University
• Jennifer Neville

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