Collective Classification Techniques: An Experimental Study

  • Tomasz KajdanowiczEmail author
  • Przemyslaw Kazienko
  • Marcin Janczak
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 185)


Collective classification is the area in machine learning, in which unknown nodes in the network are classified based on the classes assigned to the known nodes and the network structure only. Three collective classification algorithms were described and examined in the paper: Iterative Classification (ICA), Gibbs Sampling (GS) and Loopy Belief Propagation (LBP). Experiments on various networks revealed that greater number of output classes decreases classification accuracy,GS provides better results than ICA and LBP outperforms others for dense structures while it is worse for sparse networks.


Gibbs Sampling Unknown Node Gibbs Sampling Algorithm Label Assignment Loopy Belief Propagation 
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 2013

Authors and Affiliations

  • Tomasz Kajdanowicz
    • 1
  • Przemyslaw Kazienko
    • 1
  • Marcin Janczak
    • 1
  1. 1.Department of Computer Science and ManagementWroclaw University of TechnologyWroclawPoland

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