A new classification-rule pruning procedure for an ant colony algorithm

Authors:
Allen Chan;Alex Freitas
Affiliations:
Computing Laboratory, University of Kent, Canterbury, UK;Computing Laboratory, University of Kent, Canterbury, UK
Venue:
EA'05 Proceedings of the 7th international conference on Artificial Evolution
Year:
2005

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Abstract

This work proposes a new rule pruning procedure for Ant-Miner, an Ant Colony algorithm that discovers classification rules in the context of data mining. The performance of Ant-Miner with the new pruning procedure is evaluated and compared with the performance of the original Ant-Miner across several datasets. The results show that the new pruning procedure has a mixed effect on the performance of Ant-Miner. On one hand, overall it tends to decrease the classification accuracy more often than it improves it. On the other hand, the new pruning procedure in general leads to the discovery of classification rules that are considerably shorter, and so simpler (more easily interpretable by the users) than the rules discovered by the original Ant-Miner.