A Stigmergy Based Approach to Data Mining

  • Manu De Backer
  • Raf Haesen
  • David Martens
  • Bart Baesens
Conference paper

DOI: 10.1007/11589990_123

Part of the Lecture Notes in Computer Science book series (LNCS, volume 3809)
Cite this paper as:
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

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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Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Manu De Backer
    • 1
  • Raf Haesen
    • 1
  • David Martens
    • 1
  • Bart Baesens
    • 1
    • 2
  1. 1.Department of Applied Economic SciencesK.U.Leuven, BelgiumLeuvenBelgium
  2. 2.School of Management, United KingdomUniversity of SouthamptonHighfield SouthamptonUnited Kingdom

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