Abstract
In this paper, we report on the use of ant systems in the data mining field capable of extracting comprehensible classifiers from data. The ant system used is a \({\mathcal MAX}-{\mathcal MIN}\) ant system which differs from the originally proposed ant systems in its ability to explore bigger parts of the solution space, yielding better performing rules. Furthermore, we are able to include intervals in the rules resulting in less and shorter rules. Our experiments show a significant improvement of the performance both in accuracy and comprehensibility, compared to previous data mining techniques based on ant systems and other state-of-the-art classification techniques.
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© 2005 Springer-Verlag Berlin Heidelberg
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De Backer, M., Haesen, R., Martens, D., Baesens, B. (2005). A Stigmergy Based Approach to Data Mining. In: Zhang, S., Jarvis, R. (eds) AI 2005: Advances in Artificial Intelligence. AI 2005. Lecture Notes in Computer Science(), vol 3809. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11589990_123
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DOI: https://doi.org/10.1007/11589990_123
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-30462-3
Online ISBN: 978-3-540-31652-7
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