Abstract
The Particle Swarm Optimization(PSO) algorithm,is a robust stochastic evolutionary algorithm based on the movement and intelligence of swarms. In this paper, a PSO-based algorithm for classification rule mining is presented. Compared with the Ant-Miner and ESIA in public domain data sets,the proposed method achieved higher predictive accuracy and much smaller rule list than Ant-Miner and ESIA.
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Wang, Z., Sun, X., Zhang, D. (2006). Classification Rule Mining Based on Particle Swarm Optimization. In: Wang, GY., Peters, J.F., Skowron, A., Yao, Y. (eds) Rough Sets and Knowledge Technology. RSKT 2006. Lecture Notes in Computer Science(), vol 4062. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11795131_63
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DOI: https://doi.org/10.1007/11795131_63
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-36297-5
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