JACIII Vol.13 No.3 pp. 338-346
doi: 10.20965/jaciii.2009.p0338


HACO2 Method for Evolving Hyperbox Classifiers with Ant Colony Optimization

Guilherme N. Ramos, Fangyan Dong, and Kaoru Hirota

Department of Computational Intelligence and Systems Science, Tokyo Institute of Technology, G3-49, 4259 Nagatsuta, Midori-ku, Yokohama-city 226-8502, Japan

November 13, 2008
February 19, 2009
May 20, 2009
classification, ant colony optimization, hyperboxes, pattern recognition, software quality

A method, called HACO2 (Hyperbox classifier with Ant Colony Optimization – type 2), is proposed for evolving a hyperbox classifier using the ant colony meta-heuristic. It reshapes the hyperboxes in a near-optimal way to better fit the data, improving the accuracy and possibly indicating its most discriminative features. HACO2 is validated using artificial 2D data showing over 90% accuracy. It is also applied to the benchmark iris data set (4 features), providing results with over 93% accuracy, and to the MIS data set (11 features), with almost 85% accuracy. For these sets, the two most discriminative features obtained from the method are used in simplified classifiers which result in accuracies of 100% for the iris and 83% for the MIS data sets. Further modifications (automatic parameter setting), extensions (initialization short comings) and applications are discussed.

Cite this article as:
Guilherme N. Ramos, Fangyan Dong, and Kaoru Hirota, “HACO2 Method for Evolving Hyperbox Classifiers with Ant Colony Optimization,” J. Adv. Comput. Intell. Intell. Inform., Vol.13, No.3, pp. 338-346, 2009.
Data files:
  1. [1] L. de C. T. Gomes, F. J. V. Zuben, and P. Moscato, “A proposal for direct-ordering gene expression data by self-organising maps,” Applied Soft Computing, Vol.5, No.1, pp. 11-21, Dec. 2004.
  2. [2] G. N. Ramos, Y. Hatakeyama, F. Dong, and K. Hirota, “Hyperbox Clustering with Ant Colony Optimization (HACO) Method And its Application to Medical Risk Profile Recognition,” Applied Soft Computing Vol.9, Issue 2, pp. 632-640, March 2009.
  3. [3] S. Yella, N. K. Gupta, and M. S. Dougherty, “Comparison of pattern recognitionnext term techniques for the classification of impact acoustic emissions,” Transportation Research Part C: Emerging Technologies, Vol.15, No.6, pp. 345-360, 2007.
  4. [4] A. Lumini and L. Nanni, “Two-class fingerprint matcher,” Pattern Recognition, Vol.39, No.4, pp. 714-716, April 2006.
  5. [5] R. O. Duda, P. E. Hart, and D. G. Stork, “Pattern Classification,” Wiley-Interscience, 2000.
  6. [6] P. Simpson, “Fuzzy min-max neural networks – Part 1: Classification,” IEEE Transactions on Neural Networks, Vol.3, pp. 776-786, 1992.
  7. [7] P. Simpson, “Fuzzy min-max neural networks – Part 2: Clustering,” IEEE Transactions on Fuzzy Systems, Vol.1, p. 32, 1993.
  8. [8] M. Dorigo, V. Maniezzo, and A. Corloni, “Ant System,” IEEE Transactions on Systems, Man, and Cybernetics-Part B, Vol.26, pp. 29-41, 1996.
  9. [9] M. Dorigo and T. Stützle, “Ant Colony Optimization,” The MIT Press, 2004.
  10. [10] M. Dorigo and G. Di Caro, “Ant Colony Optimization Meta-heuristic,” New Ideas in Optimization, McGraw-Hill, pp. 11-32, 1999.
  11. [11] L. M. Gambardella and M. Dorigo, “An Ant Colony System Hybridized with a New Local Search for the Sequential Ordering Problem,” INFORMS Journal on Computing, Vol.12, pp. 237-255, 2000.
  12. [12] J. L. Deneubourg et al., “The self-organizing exploratory pattern of the argentine ant,” Journal of Insect Behavior, Vol.3, pp. 159-168, 1990.
  13. [13] S. Goss et al., “Self-organized shortcuts in the Argentine ant,” Naturwissenschaften, Vol.76, pp. 579-581, 1989.
  14. [14] T. M. Mitchell, “Machine Learning,” Mcgraw-Hill, 1997.
  15. [15] W. Pedrycz and S. Giancarlo, “Genetic granular classifiers in modeling software quality,” Journal of Systems and Software, Vol.76, pp. 277-285, 2005.
  16. [16] W.J. Gutjahr, “A Graph-based Ant system and its convergence,” Future Generation Computer Systems, Vol.16, No.9, pp. 873-888, 2000.
  17. [17] G. Di Caro and M. Dorigo, “AntNet: Distributed Stigmergic Control for Communication Networks,” Journal of Artificial Intelligence Research, Vol.9, pp. 317-365, 1998.
  18. [18] M. Dorigo, M. Birattari, and T. Stützle, “Artificial Ants as a Computational Intelligence Technique,” IEEE Computational Intelligence Magazine, 2006.
  19. [19] N. Monmarché, M. Slimane, and G. Venturini, “AntClass: discovery of clusters in numeric data by an hybridization of an ant colony with the Kmeans algorithm,” Technical Report No.213, E3I, Laboratorie d'Informatique, University of Tours, 1999.
  20. [20] J. C. Bezdek, “Pattern Recognition with Fuzzy Objective Function Algorithms,” Plenun, 1981.
  21. [21] G. N. Ramos, F. Dong, and K. Hirota, “Hyperbox Classifier with Ant Colony Optimization,” Proc. of SCIS & ISIS 2008, pp. 1714-1718, September 17-21, Nagoya, Japan, 2008.
  22. [22] R. R. Yager and L. A. Zadeh, “Fuzzy Sets, Neural Networks, and Soft Computing,” John Wiley & Sons, Inc. 1994.

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