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Erschienen in:
2005 | OriginalPaper | Buchkapitel
Classification Rule Mining with an Improved Ant Colony Algorithm
verfasst von : Ziqiang Wang, Boqin Feng
Erschienen in: AI 2004: Advances in Artificial Intelligence
Verlag: Springer Berlin Heidelberg
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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.