Table of Contents
Journal of Artificial Evolution and Applications
Volume 2008, Article ID 316145, 11 pages
http://dx.doi.org/10.1155/2008/316145
Research Article

A Hybrid PSO/ACO Algorithm for Discovering Classification Rules in Data Mining

Computing Laboratory, University of Kent, Canterbury, Kent CT2 7NF, UK

Received 20 July 2007; Accepted 6 March 2008

Academic Editor: Jim Kennedy

Copyright © 2008 Nicholas Holden and Alex A. Freitas. 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. I. H. Witten and E. Frank, Data Mining: Practical Machine Learning Tools and Techniques, Morgan Kaufmann, San Francisco, Calif, USA, 2nd edition, 2005.
  2. U. M. Fayyad, G. Piatetsky-Shapiro, and P. Smyth, “From data mining to knowledge discovery: an overview,” in Advances in Knowledge Discovery and Data Mining, pp. 1–34, AAAI, Menlo Park, Calif, USA, 1996. View at Google Scholar
  3. J. Kennedy, R. C. Eberhart, and Y. Shi, Swarm Intelligence, Morgan Kaufmann/Academic Press, San Francisco, CA, USA, 2001.
  4. D. Bratton and J. Kennedy, “Defining a standard for particle swarm optimization,” in Proceedings of the IEEE Swarm Intelligence Symposium (SIS '07), pp. 120–127, Honolulu, Hawaii, USA, April 2007. View at Publisher · View at Google Scholar
  5. M. Dorigo and T. Stützle, Ant Colony Optimization, The MIT Press, Cambridge, Mass, USA, 2004.
  6. N. Holden and A. A. Freitas, “A hybrid particle swarm/ant colony algorithm for the classification of hierarchical biological data,” in Proceedings of the IEEE Swarm Intelligence Symposium (SIS '05), pp. 100–107, Pasadena, Calif, USA, June 2005. View at Publisher · View at Google Scholar
  7. N. Holden and A. A. Freitas, “Hierarchical classification of G-protein-coupled receptors with a PSO/ACO algorithm,” in Proceedings of the IEEE Swarm Intelligence Symposium (SIS '06), pp. 77–84, Indianapolis, Ind, USA, May 2006.
  8. T. Sousa, A. Silva, and A. Neves, “Particle swarm based data mining algorithms for classification tasks,” Parallel Computing, vol. 30, no. 5-6, pp. 767–783, 2004. View at Publisher · View at Google Scholar
  9. I. De Falco, A. Della Cioppa, and E. Tarantino, “Facing classification problems with particle swarm optimization,” Applied Soft Computing, vol. 7, no. 3, pp. 652–658, 2007. View at Publisher · View at Google Scholar
  10. A. A. Freitas, R. S. Parpinelli, and H. S. Lopes, “Ant colony algorithms for data classification,” in Encyclopedia of Information Science and Technology, M. Khosrou-Pour, Ed., pp. 420–424, IGI Publishing, Hershey, Pa, USA, 2nd edition, 2005. View at Google Scholar
  11. R. S. Parpinelli, H. S. Lopes, and A. A. Freitas, “Data mining with an ant colony optimization algorithm,” IEEE Transactions on Evolutionary Computation, vol. 6, no. 4, pp. 321–332, 2002. View at Publisher · View at Google Scholar
  12. M. Dorigo and K. Socha, “An introduction to ant colony optimization,” in Handbook of Approximation Algorithms and Metaheuristics, T. F. Gonzalez, Ed., pp. 26.1–26.14, Chapman & Hall/CRC, Boca Raton, Fla, USA, 2007. View at Google Scholar
  13. K. V. Price, R. M. Storn, and J. A. Lampinen, Differential Evolution: A Practical Approach to Global Optimization, Springer, Berlin, Germany, 2005.
  14. N. Holden and A. A. Freitas, “A hybrid PSO/ACO algorithm for classification,” in Proceedings of the 9th Genetic and Evolutionary Computation Conference Workshop on Particle Swarms: The Second Decade (GECCO '07), pp. 2745–2750, ACM Press, London, UK, July 2007. View at Publisher · View at Google Scholar
  15. J. R. Quinlan, C4.5: Programs for Machine Learning, Morgan Kaufmann, San Francisco, Calif, USA, 1993.
  16. G. L. Pappa, Automatically evolving rule induction algorithms with grammar-based genetic programming [Ph.D. thesis, Computing Laboratory, University of Kent], Canterbury, UK, 2007.
  17. J. Kennedy and R. Mendes, “Population structure and particle swarm performance,” in Proceedings of the IEEE Conference on Evolutionary Computation (CEC '02), pp. 1671–1676, Honolulu, Hawaii, USA, May 2002. View at Publisher · View at Google Scholar
  18. E. Mezura-Montes, J. Velázquez-Reyes, and C. A. Coello Coello, “A comparative study of differential evolution variants for global optimization,” in Proceedings of the 8th Annual Conference on Genetic and Evolutionary Computation (GECCO '06), vol. 1, pp. 485–492, ACM Press, Seattle, Wash, USA, July 2006. View at Publisher · View at Google Scholar
  19. D. J. Hand, Construction and Assessment of Classification Rules, John Wiley & Sons, New York, NY, USA, 1997.
  20. P. Clark and R. Boswell, “Rule induction with CN2: some recent improvements,” in Proceedings of the 5th European Working Session on Learning (EWSL '91), pp. 151–163, Porto, Portugal, March 1991. View at Publisher · View at Google Scholar
  21. D. J. Newman, S. Hettich, C. L. Blake, and C. J. Merz, “UCI repository of machine learning databases,” 1998, http://www.ics.uci.edu/~mlearn/MLRepository.html.
  22. Differential Evolution Java Implementation, July 2007, http://www.icsi.berkeley.edu/~storn/code.html#java.