Skip to main content
SpringerLink
Log in

ACO Hybrid Algorithm for Document Classification System

  • Chapter
Innovations in Swarm Intelligence

Part of the book series: Studies in Computational Intelligence ((SCI,volume 248))

Abstract

In the present study an ACO algorithm is adopted as a part of a document classification system that classifies documents written in Greek, in thematic categories. The main purpose of the ACO module is to create a word map that will assist in the representation of the documents in the pattern space. The word map creation algorithm proposed involves additional deterministic sub-routines and aims at clustering together into groups thematically-related words. The performance of the proposed system is compared with an alternative system implementation that is based on the established SOM neural network.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 109.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Chen, X., Li, Y.: A Modified PSO Structure Resulting in High Exploration Ability With Convergence Guaranteed. IEEE Transactions on Systems, Man & Cybernetics, Part B: Cybernetics 37(5), 1271–1289 (2007)

    Article  Google Scholar 

  2. Dorigo, M., Gambardella, M.: Ant Colony System: A Cooperative Learning Approach to the Travelling Salesman Problem. IEEE Transactions on Evolutionary Computation 1(1), 53–66 (1997)

    Article  Google Scholar 

  3. Drucker, H., Wu, D., Vapnik, V.: Support Vector Machines for Spam categorization. IEEE Trans. on Neural Networks 10(5), 1048–1054 (1999)

    Article  Google Scholar 

  4. Dussutour, A., Fourcassie, V., Helbing, D., Deneubourg, J.-L.: Optimal Traffic Organisation in Ants under Crowded Conditions. Nature 478(6978), 70–73 (2004)

    Article  Google Scholar 

  5. Duda, R.O., Hart, P.E., Stork, D.G.: Pattern Classification, 2nd edn. Wiley, New York (2001)

    MATH  Google Scholar 

  6. Everitt, B.S., Landau, S., Leese, M.: Cluster Analysis. Hodder Arnold Publication (2001)

    Google Scholar 

  7. Georgakis, A., Kotropoulos, C., Xafopoulos, A., Pitas, I.: Marginal median SOM for document organization and retrieval. Neural Networks 17(3), 365–377 (2004)

    Article  MATH  Google Scholar 

  8. Freeman, R.T., Yin, H.: Web content management by self-organization. IEEE Transactions on Neural Networks 16(5), 1256–1268 (2005)

    Article  Google Scholar 

  9. Haykin, S.: NEURAL NETWORKS: A Comprehensive foundation, 2nd edn. Prentice-Hall, Englewood Cliffs (1999)

    MATH  Google Scholar 

  10. Igel, C., Huesken, M.: Empirical evaluation of the improved Rprop learning algorithm. Neurocomputing 50, 105–123 (2003)

    Article  MATH  Google Scholar 

  11. Kaski, S.: Dimensionality Reduction by Random mapping: Fast Similarity Computation for Clustering. In: Proceedings of IJCNN 1998 Conference, International Joint Conference on Neural Networks, vol. 1, pp. 413–418 (1998)

    Google Scholar 

  12. Kennedy, J., Eberhart, R.C.: Swarm Intelligence. Morgan Kaufmann, San Francisco (2001)

    Google Scholar 

  13. Kohonen, T.: Self-Organizing Map, 2nd edn. Springer, Berlin (1997)

    Google Scholar 

  14. Kohonen, T., Somervuo, P.: Self-organizing maps of symbol strings. Neurocomputing 21(1-3), 19–30 (1998)

    Article  MATH  Google Scholar 

  15. Kohonen, T., Kaski, S., Lagus, K., Salojarvi, H.J., Patero, V., Saarela, A.: Self Organisation of a Massive Document Collection. IEEE Transactions on Neural Networks 11(3), 574–585 (2000)

    Article  Google Scholar 

  16. Lagus, K., Kaski, S., Kohonen, T.: Mining Massive Document Collections by the WEBSOM Method. Information Sciences 163(1-3), 135–156 (2004)

    Article  Google Scholar 

  17. Lessing, L., Dumitrescu, I., Stützle, T.: A comparison between ACO algorithms for the set covering problem. In: Dorigo, M., Birattari, M., Blum, C., Gambardella, L.M., Mondada, F., Stützle, T. (eds.) ANTS 2004. LNCS, vol. 3172, pp. 1–12. Springer, Heidelberg (2004)

    Google Scholar 

  18. MacKay, D.: Information Theory, Inference, and Learning Algorithms. Cambridge University Press, Cambridge (2003)

    MATH  Google Scholar 

  19. Manning, C.D., Schütze, H.: Foundations of Statistical Natural Language Processing. MIT Press, Cambridge (1999)

    MATH  Google Scholar 

  20. Martens, D., De Backer, M., Haesen, R., Baesens, B., Mues, C., Vanthienen, J.: Ant-Based Approach to the Knowledge Fusion Problem. In: Dorigo, M., Gambardella, L.M., Birattari, M., Martinoli, A., Poli, R., Stützle, T. (eds.) ANTS 2006. LNCS, vol. 4150, pp. 84–95. Springer, Heidelberg (2006)

    Chapter  Google Scholar 

  21. Nguyen, D., Widrow, B.: Improving the learning speed of 2-layer neural networks by choosing initial values of adaptive weights. In: Proceedings of the International Joint Conference on Neural Networks, vol. 3, pp. 21–26 (1990)

    Google Scholar 

  22. Papageorgiou, H., Prokopidis, P., Giouli, V., Piperidis, S.: A Unified PoS Tagging Architecture and its Application to Greek. In: Second International Conference on Language Resources and Evaluation Proceedings, Athens, Greece, vol. 3, pp. 1455–1462 (2000)

    Google Scholar 

  23. Riedmiller, M., Braun, H.: A direct adaptive method for faster backpropagation learning: the RPROP algorithm. In: Proceedings of the IEEE International Conference on Neural Networks, San Francisco, CA, pp. 586–591 (1993)

    Google Scholar 

  24. Rumelhart, D.E., Hinton, G.E., Williams, R.J.: Learning representations by back-propagating errors. Nature 323, 533–536 (1986)

    Article  Google Scholar 

  25. Salton, G., Buckley, C.: Term-weighting approaches in automatic text retrieval. Information Processing & Management 24(5), 513–523 (1988)

    Article  Google Scholar 

  26. Stützle, T., Hoos, H.: MAX-MIN Ant System. Future Generation Computer Systems 16, 889–914 (2000)

    Article  Google Scholar 

  27. Tsimboukakis, N., Tambouratzis, G.: Self-Organizing Word Map for Context-Based Document Classification. In: Proceedings of the WSOM 2007 International Workshop on Self-Organizing Maps, Bielefeld, Germany (2007)

    Google Scholar 

  28. Tsimboukakis, N., Tambouratzis, G.: Document classification system based on HMM word map. In: CSTST 2008, 5th International Conference on Soft Computing as Transdisciplinary Science and Technology, Paris, France (2008)

    Google Scholar 

  29. Tweedie, F., Singh, S., Holmes, D.: An Introduction to Neural Networks in Stylometry. Research in Humanities Computing 5, 249–263 (1996)

    Google Scholar 

  30. Vesanto, J.: Neural Network Tool for Data Mining: SOM Toolbox. In: Proceedings of Symposium on Tool Environments and Development Methods for Intelligent Systems (TOOLMET 2000), Finland, pp. 184–196 (2000)

    Google Scholar 

  31. Visine, A.L., de Castro, L.N., Hruschka, E.R., Gudwin, R.R.: Towards Improving Clustering Ants: An Adaptive Ant Clustering Algorithm. Informatica 25, 143–154 (2005)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Institute for Language and Speech Processing, 6 Artemidos & Epidavrou Str. Amaroussion, Greece

    Nikos Tsimboukakis & George Tambouratzis

Authors
  1. Nikos Tsimboukakis
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. George Tambouratzis
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. University of South Australia, Adelaide, Australia

    Chee Peng Lim  & Lakhmi C. Jain  & 

  2. Soft Computing Laboratory, Department of computer Science, Yonsei University, 120-749, Seoul, Korea

    Satchidananda Dehuri

Rights and permissions

Reprints and permissions

Copyright information

© 2009 Springer-Verlag Berlin Heidelberg

About this chapter

Cite this chapter

Tsimboukakis, N., Tambouratzis, G. (2009). ACO Hybrid Algorithm for Document Classification System. In: Lim, C.P., Jain, L.C., Dehuri, S. (eds) Innovations in Swarm Intelligence. Studies in Computational Intelligence, vol 248. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04225-6_11

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-04225-6_11

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-04224-9

  • Online ISBN: 978-3-642-04225-6

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation