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:
G. Ramos, F. Dong, and K. 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.
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