Classification rule mining with an improved ant colony algorithm

Authors:
Ziqiang Wang;Boqin Feng
Affiliations:
Computer Science Department, Xi'an Jiaotong University, Xi'an, P.R.C (China);Computer Science Department, Xi'an Jiaotong University, Xi'an, P.R.C (China)
Venue:
AI'04 Proceedings of the 17th Australian joint conference on Advances in Artificial Intelligence
Year:
2004

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Abstract

This paper presents an improvement ant colony optimization algorithm for mining classification rule called ACO-Miner The goal of ACO-Miner is to effectively provide intelligible classification rules which have higher predictive accuracy and simpler rule list based on Ant-Miner Experiments on data sets from UCI data set repository were made to compare the performance of ACO-Miner with Ant-Miner The results show that ACO-Miner performs better than Ant-Miner with respect to predictive accuracy and rule list mined simplicity.