Semi-Supervised Classification Using Tree-Based Self-Organizing Maps

  • César A. Astudillo
  • B. John Oommen
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7106)


This paper presents a classifier which uses a tree-based Neural Network (NN), and uses both, unlabeled and labeled instances. First, we learn the structure of the data distribution in an unsupervised manner. After convergence, and once labeled data become available, our strategy tags each of the clusters according to the evidence provided by the instances. Unlike other neighborhood-based schemes, our classifier uses only a small set of representatives whose cardinality can be much smaller than that of the input set. Our experiments show that, on average, the accuracy of such classifier is reasonably comparable to those obtained by some of the state-of-the-art classification schemes that only use labeled instances during the training phase. The experiments also show that improved levels of accuracy can be obtained by imposing trees with a larger number of nodes.


Hierarchical SOM Topology-Based Self-Organization Pattern Recognition Semi-Supervised Learning 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • César A. Astudillo
    • 1
  • B. John Oommen
    • 2
  1. 1.Universidad de TalcaCuricóChile
  2. 2.School of Computer ScienceCarleton UniversityOttawaCanada

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