A stigmergy based approach to data mining

Authors:
Manu De Backer;Raf Haesen;David Martens;Bart Baesens
Affiliations:
Department of Applied Economic Sciences, K.U.Leuven, Belgium, Leuven, Belgium;Department of Applied Economic Sciences, K.U.Leuven, Belgium, Leuven, Belgium;Department of Applied Economic Sciences, K.U.Leuven, Belgium, Leuven, Belgium;Department of Applied Economic Sciences, K.U.Leuven, Belgium, Leuven, Belgium
Venue:
AI'05 Proceedings of the 18th Australian Joint conference on Advances in Artificial Intelligence
Year:
2005

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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.