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.
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Wang, Z., Feng, B. (2004). Classification Rule Mining with an Improved Ant Colony Algorithm. In: Webb, G.I., Yu, X. (eds) AI 2004: Advances in Artificial Intelligence. AI 2004. Lecture Notes in Computer Science(), vol 3339. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30549-1_32
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DOI: https://doi.org/10.1007/978-3-540-30549-1_32
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