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Collective Classification Using Heterogeneous Classifiers

  • Conference paper
Machine Learning and Data Mining in Pattern Recognition (MLDM 2011)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 6871))

Abstract

Collective classification algorithms have been used to improve classification performance when network training data with content, link and label information and test data with content and link information are available. Collective classification algorithms use a base classifier which is trained on training content and link data. The base classifier inputs usually consist of the content vector concatenated with an aggregation vector of neighborhood class information. In this paper, instead of using a single base classifier, we propose using different types of base classifiers for content and link. We then combine the content and link classifier outputs using different classifier combination methods. Our experiments show that using heterogeneous classifiers for link and content classification and combining their outputs gives accuracies as good as collective classification. Our method can also be extended to collective classification scenarios with multiple types of content and link.

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Cataltepe, Z., Sonmez, A., Baglioglu, K., Erzan, A. (2011). Collective Classification Using Heterogeneous Classifiers. In: Perner, P. (eds) Machine Learning and Data Mining in Pattern Recognition. MLDM 2011. Lecture Notes in Computer Science(), vol 6871. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23199-5_12

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  • DOI: https://doi.org/10.1007/978-3-642-23199-5_12

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-23198-8

  • Online ISBN: 978-3-642-23199-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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