An Ant Colony Optimization Algorithm for Learning Classification Rules

Authors:
Junzhong Ji;Ning Zhang;Chunnian Liu;Ning Zhong
Affiliations:
Beijing University of Technology, China;Beijing University of Technology, China;Beijing University of Technology, China;Maebashi Institute of Technology, Japan
Venue:
WI '06 Proceedings of the 2006 IEEE/WIC/ACM International Conference on Web Intelligence
Year:
2006

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Abstract

Ant Colony Optimization (ACO) algorithm has been applied to data mining recently. Aiming at Ant Miner, a classification rule learning algorithm based on ACO, this paper presents an enhanced Ant Miner, which includes two main contributions. Firstly, a rule punishing operator is employed to reduce the number of rules and the number of conditions. Secondly, an adaptive state transition rule and a mutation operator are applied to the algorithm to speed up the convergence rate. The results of experiments on some data sets demonstrate that the Enhanced Ant-Miner can quickly discover better classification rules which have roughly competitive predicative accuracy and short rules.