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

Paper:

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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

Received:
November 13, 2008
Accepted:
February 19, 2009
Published:
May 20, 2009
Creative Commons License
Keywords:
classification, ant colony optimization, hyperboxes, pattern recognition, software quality
Abstract
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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