Abstract
This paper describes the implementation of Data Mining tasks using Particle Swarm Optimisers. The object of our research has been to apply such algorithms to classification rule discovery. Results, concerning accuracy and speed performance, were empirically compared with another evolutionary algorithm, namely a Genetic Algorithm and with J48 – a Java implementation of C4.5. The data sets used for experimental testing have already been widely used and proven reliable for testing other Data Mining algorithms. The obtained results seem to indicate that Particle Swarm Optimisers are competitive with other evolutionary techniques, and could come to be successfully applied to more demanding problem domains.
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Sousa, T., Silva, A., Neves, A. (2003). A Particle Swarm Data Miner. In: Pires, F.M., Abreu, S. (eds) Progress in Artificial Intelligence. EPIA 2003. Lecture Notes in Computer Science(), vol 2902. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-24580-3_12
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DOI: https://doi.org/10.1007/978-3-540-24580-3_12
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-20589-0
Online ISBN: 978-3-540-24580-3
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