Ant Colony reduction with modified rules generation for rough classification model
DOI: 10.1109/ISDA.2010.5687055 Conference: 10th International Conference on Intelligent Systems Design and Applications, ISDA 2010, November 29 - December 1, 2010, Cairo, Egypt
In this paper we propose a rough classification modeling algorithm based on Ant Colony Optimization (ACO) reduction. We used ACO to compute the rough set reduct and later a modified rules generation method is employed to generate the classification rules. The rules generation algorithm used is the simplification of the Default Rules Generation Framework (DRGF) in order to fit with the ACO reduct. The performance of the proposed classifier is compared with the DRGF based classifier using genetic reduction. The experimental results show that the ACO-Rough performs better with higher classification accuracy and fewer number of rules.
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