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Computational Intelligence and Neuroscience
Volume 2011 (2011), Article ID 121787, 11 pages
http://dx.doi.org/10.1155/2011/121787
Research Article

Multistrategy Self-Organizing Map Learning for Classification Problems

Soft Computing Research Group, Faculty of Computer Science and Information System, Universiti Teknologi Malaysia, Skudai, 81300 Johor, Malaysia

Received 12 January 2011; Revised 21 April 2011; Accepted 23 June 2011

Academic Editor: Francois Benoit Vialatte

Copyright © 2011 S. Hasan and S. M. Shamsuddin. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. M. Negnevitsky, Artificial Intelligence: A Guide to Intelligent Systems, Addison Wesley, Harlow, England, 2nd edition, 2005.
  2. S. Chattopadhyay, D. Jhajharia, and G. Chattopadhyay, “Univariate modelling of monthly maximum temperature time series over northeast India: neural network versus Yule-Walker equation based approach,” Meteorological Applications, vol. 18, no. 1, pp. 70–82, 2011. View at Publisher · View at Google Scholar
  3. Y. L. Wong and S. M. Shamsuddin, “Writer identification for Chinese handwriting,” International Journal of Advances in Soft Computing and Its Applications (IJASCA), vol. 2, no. 2, 2010.
  4. S. Hasan and M. N. M. Sap, “Pest clustering with self organizing map for rice productivity,” International Journal of Advances in Soft Computing and Its Applications (IJASCA), vol. 2, no. 2, 2010.
  5. S. M. Shamsuddin, M. N. Sulaiman, and M. Darus, “An improved error signal for the backpropagation model for classification problems,” International Journal of Computer Mathematics, vol. 76, no. 3, pp. 297–305, 2001. View at Scopus
  6. C. Surajit and B. Goutami, “Artificial neural network with backpropagation learning to predict mean monthly total ozone in Arosa, Switzerland,” International Journal of Remote Sensing, vol. 28, no. 20, pp. 4471–4482, 2007. View at Publisher · View at Google Scholar · View at Scopus
  7. J. Kennedy and R. C. Eberhart, “Particle swarm optimization,” in Proceedings of the International Conference on Neural Networks, vol. 4, pp. 1942–1948, IEEE service center, Piscataway, NJ, USA, 1995.
  8. Y. Shi and R. C. Eberhart, “A modified particle swarm optimizer,” in Proceedings of the IEEE International Conference on Evolutionary Computation, pp. 69–73, IEEE Press, Piscataway, NJ, USA, 1998.
  9. X. Xiao, E. R. Dow, R. Eberhart, Z. B. Miled, and R. J. Oppelt, “Gene-Clustering Using Self-Organizing Maps and Particle Swarm Optimization,” in Proceedings of the IEEE International Parallel and Distributed Processing Symposium (IPDPS '03), IEEE Press, Nice, France, April 2003.
  10. X. Xiao, E. R. Dow, R. Eberhart, Z. B. Miled, and R. J. Oppelt, “A hybrid self-organizing maps and particle swarm optimization approach,” Concurrency Computation Practice and Experience, vol. 16, no. 9, pp. 895–915, 2004. View at Publisher · View at Google Scholar · View at Scopus
  11. M. O'neill and A. Brabazon, “A particle swarm algorithm for unsupervised learning,” in Proceedings of the Self-Organizing Swarm (SOSwarm '06), IEEE World Congress on Computational Intelligence, Vancouver, Canada, July 2006.
  12. K. Chandramouli, “Particle swarm optimization and self organizing maps based image classifier,” in Proceedings of the IEEE 2nd International Workshop on Semantic Media Adaptation and Personalization, pp. 225–228, December 2007. View at Publisher · View at Google Scholar
  13. F. Forkan and S.M Shamsuddin, “Kohonen-swarm algorithm for unstructured data in surface reconstruction,” in Proceedings of the IEEE 5th International Conference on Computer Graphics, Imaging and Visualization, 2008.
  14. A. Sharma and C. W. Omlin, “Performance comparison of Particle Swarm Optimization with traditional clustering algorithms used in Self Organizing Map,” International Journal of Computational Intelligence, vol. 5, no. 1, pp. 1–12, 2009.
  15. A. Özçift, M. Kaya, A. Gülten, and M. Karabulut, “Swarm optimized organizing map (SWOM): a swarm intelligence basedoptimization of self-organizing map,” Expert Systems with Applications, vol. 36, no. 7, pp. 10640–10648, 2009. View at Publisher · View at Google Scholar · View at Scopus
  16. B. Fritzke, “Growing cell structures-A self-organizing network for unsupervised and supervised learning,” Neural Networks, vol. 7, no. 9, pp. 1441–1460, 1994. View at Scopus
  17. A. L. Hsu, I. Saeed, and K. Halgamuge, “Dynamic self-organising maps: theory, methods and applications,” Foundations of Computational, Intelligence Volume 1, vol. 201, pp. 363–379, 2009. View at Publisher · View at Google Scholar · View at Scopus
  18. C. K. K. Chan, A. L. Hsu, S. L. Tang, and S. K. Halgamuge, “Using growing self-organising maps to improve the binning process in environmental whole-genome shotgun sequencing,” Journal of Biomedicine and Biotechnology, vol. 2008, no. 1, 2008. View at Publisher · View at Google Scholar · View at PubMed
  19. J. C. Créput, A. Koukam, and A. Hajjam, “Self-Organizing Maps in Evolutionary Approach for the Vehicle Routing Problem with Time Windows,” IJCSNS International Journal of Computer Science and Network Security, vol. 7, no. 1, pp. 103–110, 2007.
  20. S. T. Khu, H. Madsen, and F. di Pierro, “Incorporating multiple observations for distributed hydrologic model calibration: an approach using a multi-objective evolutionary algorithm and clustering,” Advances in Water Resources, vol. 31, no. 10, pp. 1387–1398, 2008. View at Publisher · View at Google Scholar · View at Scopus
  21. K. Eisuke, S. Kan, and Z. Fei, “Investigation of self-organizing map for genetic algorithm,” Advances in Engineering Software, vol. 41, no. 2, pp. 148–153, 2010. View at Publisher · View at Google Scholar · View at Scopus
  22. D. Brennan and M. M. van Hulle, “Comparison of flat SOM with spherical SOM. A case study,” in The Self-Organizing Maps and the Development—From Medicine and Biology to the Sociological Field, H. Tokutaka, M. Ohkita, and K. Fujimura, Eds., pp. 31–41, Springer, Tokyo, Japan, 2007.
  23. C. Hung, “A constrained neural learning rule for eliminating the border effect in online self-organising maps,” Connection Science, vol. 20, no. 4, pp. 1–20, 2008. View at Publisher · View at Google Scholar · View at Scopus
  24. P. K. Kihato, H. Tokutaka, M. Ohkita, et al., “Spherical and torus SOM approaches to metabolic syndrome evaluation,” in Proceedings of the ICONIP, vol. 4985 of Lecture Notes in Computer Science (LNCS), pp. 274–284, Springer, Heidelberg, Germany, 2008.
  25. H. Ritter, “Self-organizing maps on non-Euclidean spaces,” in Kohonen Maps, E. Oja and S. Kaski, Eds., pp. 95–110, Elsevier, New York, NY, USA, 1999.
  26. K. Marzouki and T. Yamakawa, “Novel algorithm for eliminating folding effect in standard SOM,” in Proceedings of the European Symposium on Artificial Neural Networks Bruges (ESANN '05), pp. 563–570, Brussels, Belgium, 2005.
  27. D. Nakatsuka and M. Oyabu, “Usefulness of spherical SOM for clustering,” in Proceedings of the 19th Fuzzy System Symposium Collected Papers, pp. 67–70, Japan, 2003.
  28. T. Matsuda, et al., “Decision of class borders on spherical SOM and its visualization neural information processing,” Lecture Notes in Computer Science, vol. 5864, pp. 802–811, 2009.
  29. L. Middleton, J. Sivaswamy, and G. Coghill, “Logo shape discrimination using the HIP framework,” in Proceedings of the 5th Biannual Conference on Artificial Neural Networks and Expert Systems (ANNES '01), pp. 59–64, 2001.
  30. Y. S. Park, J. Tison, S. Lek, J. L. Giraudel, M. Coste, and F. Delmas, “Application of a self-organizing map to select representative species in multivariate analysis: a case study determining diatom distribution patterns across France,” Ecological Informatics, vol. 1, no. 3, pp. 247–257, 2006. View at Publisher · View at Google Scholar · View at Scopus
  31. T. Kohonen, Self-Organizing Maps, vol. 30, Springer Series in Information Sciences, Berlin, Germany, 3rd edition, 2001, Extended Edition.
  32. J. Vesanto and E. Alhoniemi, “Clustering of the self-organizing map,” IEEE Transactions on Neural Networks, vol. 11, no. 3, pp. 586–600, 2000. View at Publisher · View at Google Scholar · View at PubMed · View at Scopus
  33. A. Astel, S. Tsakovski, P. Barbieri, and V. Simeonov, “Comparison of self-organizing maps classification approach with cluster and principal components analysis for large environmental data sets,” Water Research, vol. 41, no. 19, pp. 4566–4578, 2007. View at Publisher · View at Google Scholar · View at PubMed · View at Scopus
  34. Y. Wu and M. Takatsuka, “Spherical self-organizing map using efficient indexed geodesic data structure,” Neural Networks, vol. 19, no. 6, pp. 900–910, 2006. View at Publisher · View at Google Scholar · View at PubMed · View at Scopus
  35. R. C. Eberhart and Y. Shi, “Particle swarm optimization: developments, applications and resources,” in Proceedings of the IEEE Congress on Evolutionary Computation '01, Piscataway, NJ, USA, 2001, Seoul, Korea.
  36. Y. Norfadzila, Multilevel Learning in Kohonen SOM Network for Classification Problems, M.S. thesis, Faculty of Computer Science and Information System, UTM University, Johor, Malaysia, 2006.
  37. H. Ying, F. Tian-Jin, C. Jun-Kuo, D. Xiang-Qiang, and Z. Ying-Hua, “Research on some problems in Kohonen SOM algorithm,” in Proceedings of the 1st Conference On Machine Learning and Cybernatics, Beijing, China, 2002.
  38. J. Vesanto and E. Alhoniemi, “Clustering of the self-organizing map,” IEEE Transactions on Neural Networks, vol. 11, no. 3, pp. 586–600, 2000. View at Publisher · View at Google Scholar · View at PubMed · View at Scopus
  39. G. Deboeck and T. Kohonen, Visual Explorations in Finance with Self-Organizing Maps, Springer, London, UK, 1998.
  40. A. P. Engelbrecht, Computational Intelligence: An Introduction, John Wiley & Sons, New York, NY, USA, 2nd edition, 2007.
  41. M. Hagenbuchner, A. Sperduti, and A. C. Tsoi, “A self-organizing map for adaptive processing of structured data,” IEEE Transactions on Neural Networks, vol. 14, no. 3, pp. 491–505, 2003. View at Publisher · View at Google Scholar · View at PubMed · View at Scopus
  42. H. W David and G. M. William, “No free lunch theorems for optimization,” IEEE Transactions on Evolutionary Computation, vol. 1, no. 1, pp. 67–82, 1997. View at Scopus
  43. W. H. Kruskal and W. A. Wallis, “Use of ranks in one-criterion variance analysis,” Journal of the American Statistical Association, vol. 47, no. 260, pp. 583–621, 1952.