Collective Classification Using Heterogeneous Classifiers

  • Zehra Cataltepe
  • Abdullah Sonmez
  • Kadriye Baglioglu
  • Ayse Erzan
Part of the Lecture Notes in Computer Science book series (LNCS, 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.

Keywords

Synthetic Dataset Test Node Content Graph Cora Dataset Link Feature 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Zehra Cataltepe
    • 1
  • Abdullah Sonmez
    • 1
  • Kadriye Baglioglu
    • 1
  • Ayse Erzan
    • 2
  1. 1.Computer Engineering Dept.Istanbul Technical UniversityMaslakTurkey
  2. 2.Physics Dept.Istanbul Technical UniversityMaslakTurkey

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