Advertisement

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)

Abstract

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.

Keywords

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

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Astudillo, C.A., Oommen, B.J.: Imposing tree-based topologies onto self organizing maps. Information Sciences 181(18), 3798–3815 (2011)MathSciNetCrossRefGoogle Scholar
  2. 2.
    Baeza-Yates, R.A., Ribeiro-Neto, B.: Modern Information Retrieval. Addison-Wesley Longman Publishing Co., Inc., Boston (1999)Google Scholar
  3. 3.
    Dara, R., Kremer, S., Stacey, D.: Clustering unlabeled data with SOMs improves classification of labeled real-world data. In: Proc. of the 2002 International Joint Conference on Neural Networks, IJCNN 2002, vol. 3, pp. 2237–2242 (2002)Google Scholar
  4. 4.
    Demiriz, A., Bennett, K., Embrechts, M.: Semi-supervised clustering using genetic algorithms. In: Artificial Neural Networks in Engineering (ANNIE 1999), pp. 809–814 (1999)Google Scholar
  5. 5.
    Duda, R., Hart, P.E., Stork, D.G.: Pattern Classification, 2nd edn. Wiley-Interscience (2000)Google Scholar
  6. 6.
    Fawcett, T.: An introduction to ROC analysis. Pattern Recogn. Lett. 27(8), 861–874 (2006)MathSciNetCrossRefGoogle Scholar
  7. 7.
    Fayyad, U., Grinstein, G.G., Wierse, A.: Information Visualization in Data Mining and Knowledge Discovery. Morgan Kaufmann Publishers Inc., San Francisco (2001)Google Scholar
  8. 8.
    Frank, A., Asuncion, A.: UCI machine learning repository (2010), http://archive.ics.uci.edu/ml
  9. 9.
    Gabrys, B., Petrakieva, L.: Combining labelled and unlabelled data in the design of pattern classification systems. International Journal of Approximate Reasoning 35(3), 251–273 (2004), Integration of Methods and Hybrid SystemsMathSciNetCrossRefzbMATHGoogle Scholar
  10. 10.
    Jain, A.K., Murty, M.N., Flynn, P.J.: Data clustering: a review. ACM Computing Surveys 31(3), 264–323 (1999), citeseer.ist.psu.edu/jain99data.html CrossRefGoogle Scholar
  11. 11.
    Kohonen, T.: Improved versions of learning vector quantization. In: 1990 IJCNN International Joint Conference on Neural Networks, vol. 1, pp. 545–550 (June 1990)Google Scholar
  12. 12.
    Kohonen, T.: Self-Organizing Maps. Springer-Verlag New York, Inc., Secaucus (1995)CrossRefzbMATHGoogle Scholar
  13. 13.
    Lazebnik, S., Raginsky, M.: Supervised learning of quantizer codebooks by information loss minimization. IEEE Transactions on Pattern Analysis and Machine Intelligence 31(7), 1294–1309 (2009)CrossRefGoogle Scholar
  14. 14.
    Manning, C.D., Raghavan, P., Schütze, H.: An Introduction to Information Retrieval. Cambridge University Press, Cambridge (2009), http://nlp.stanford.edu/IR-book/information-retrieval-book.html zbMATHGoogle Scholar
  15. 15.
    Zhu, X., Goldberg, A.B.: Introduction to Semi-Supervised Learning. Morgan & Claypool Publishers (2009)Google Scholar

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

Personalised recommendations