Dynamic Stacked Generalization for Node Classification on Networks

Zhen Han; Alyson Wilson

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arXiv · 1610.04804

Dynamic Stacked Generalization for Node Classification on Networks

Zhen Han·Alyson Wilson

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Abstract

We propose a novel stacked generalization (stacking) method as a dynamic ensemble technique using a pool of heterogeneous classifiers for node label classification on networks. The proposed method assigns component models a set of functional coefficients, which can vary smoothly with certain topological features of a node. Compared to the traditional stacking model, the proposed method can dynamically adjust the weights of individual models as we move across the graph and provide a more versatile and significantly more accurate stacking model for label prediction on a network. We demonstrate the benefits of the proposed model using both a simulation study and real data analysis.

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BibTeX RIS

Zhen Han, Alyson Wilson. 2016-10-16. Dynamic Stacked Generalization for Node Classification on Networks. https://arxiv.org/abs/1610.04804

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