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Ant-Based Approach to the Knowledge Fusion Problem

  • David Martens
  • Manu De Backer
  • Raf Haesen
  • Bart Baesens
  • Christophe Mues
  • Jan Vanthienen
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4150)

Abstract

Data mining involves the automated process of finding patterns in data and has been a research topic for decades. Although very powerful data mining techniques exist to extract classification models from data, the techniques often infer counter-intuitive patterns or lack patterns that are logical for domain experts. The problem of consolidating the knowledge extracted from the data with the knowledge representing the experience of domain experts, is called the knowledge fusion problem. Providing a proper solution for this problem is a key success factor for any data mining application. In this paper, we explain how the AntMiner+ classification technique can be extended to incorporate such domain knowledge. By changing the environment and influencing the heuristic values, we can respectively limit and direct the search of the ants to those regions of the solution space that the expert believes to be logical and intuitive.

Keywords

Domain Expert Soft Constraint Hard Constraint Decision Table Vertex Group 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    Witten, I., Frank, E.: Data Mining: Practical machine learning tools and techniques. Morgan Kaufmann, San Francisco (2005)MATHGoogle Scholar
  2. 2.
    Feelders, A., Pardoel, M.: Pruning for monotone classification trees. In: Berthold, M.R., Lenz, H.-J., Bradley, E., Kruse, R., Borgelt, C. (eds.) IDA 2003. LNCS, vol. 2810, pp. 1–12. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  3. 3.
    Altendorf, E., Restificar, E., Dietterich, T.: Learning from sparse data by exploiting monotonicity constraints. In: Proceedings of the 21st Conference on Uncertainty in Artificial Intelligence, Edinburgh, Scotland, pp. 18–26 (2005)Google Scholar
  4. 4.
    Ben-David, A.: Monotonicity maintenance in information-theoretic machine learning algorithms. Machine Learning 19(1), 29–43 (1995)Google Scholar
  5. 5.
    Velikova, M., Daniels, H., Feelders, A.: Solving partially monotone problems with neural networks. In: Proceedings of the International Conference on Neural Networks, Vienna, Austria (2006)Google Scholar
  6. 6.
    Pazzani, M., Mani, S., Shankle, W.: Acceptance by medical experts of rules generated by machine learning. Methods of Information in Medicine 40(5), 380–385 (2001)Google Scholar
  7. 7.
    Velikova, M., Daniels, H.: Decision trees for monotone price models. Computational Management Science 1(3–4), 231–244 (2004)MATHCrossRefGoogle Scholar
  8. 8.
    Daniels, H., Velikova, M.: Derivation of monotone decision models from non-monotone data. Discussion Paper 30, Tilburg University, Center for Economic Research (2003)Google Scholar
  9. 9.
    Martens, D., De Backer, M., Haesen, R., Baesens, B.: A \(\mathcal{MAX}\)-\(\mathcal{MIN}\) ant system working towards comprehensible classifiers (Under Review)Google Scholar
  10. 10.
    Martens, D., De Backer, M., Haesen, R., Baesens, B., Holvoet, T.: Ants constructing rule-based classifiers. In: Swarm Intelligence and Data Mining. Studies in Computational Intelligence, Springer, Heidelberg (2006)Google Scholar
  11. 11.
    Parpinelli, R.S., Lopes, H.S., Freitas, A.A.: Data mining with an ant colony optimization algorithm. IEEE Transactions on Evolutionary Computation 6(4), 321–332 (2002)CrossRefGoogle Scholar
  12. 12.
    Stützle, T., Hoos, H.H.: \(\mathcal{MAX}\)-\(\mathcal{MIN}\) ant system. Future Generation Computer Systems 16(8), 889–914 (2000)CrossRefGoogle Scholar
  13. 13.
    Vanthienen, J., Mues, C., Aerts, A.: An illustration of verification and validation in the modelling phase of KBS development. Data and Knowledge Engineering 27(3), 337–352 (1998)MATHCrossRefGoogle Scholar
  14. 14.
    Hettich, S., Bay, S.D.: The uci kdd archive (1996), http://kdd.ics.uci.edu

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • David Martens
    • 1
  • Manu De Backer
    • 1
  • Raf Haesen
    • 1
  • Bart Baesens
    • 1
    • 2
  • Christophe Mues
    • 1
    • 2
  • Jan Vanthienen
    • 1
  1. 1.Department of Decision Sciences & Information ManagementK.U. LeuvenBelgium
  2. 2.School of ManagementUniversity of SouthamptonUnited Kingdom

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