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.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Witten, I., Frank, E.: Data Mining: Practical machine learning tools and techniques. Morgan Kaufmann, San Francisco (2005)
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)
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)
Ben-David, A.: Monotonicity maintenance in information-theoretic machine learning algorithms. Machine Learning 19(1), 29–43 (1995)
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)
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)
Velikova, M., Daniels, H.: Decision trees for monotone price models. Computational Management Science 1(3–4), 231–244 (2004)
Daniels, H., Velikova, M.: Derivation of monotone decision models from non-monotone data. Discussion Paper 30, Tilburg University, Center for Economic Research (2003)
Martens, D., De Backer, M., Haesen, R., Baesens, B.: A \(\mathcal{MAX}\)-\(\mathcal{MIN}\) ant system working towards comprehensible classifiers (Under Review)
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)
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)
Stützle, T., Hoos, H.H.: \(\mathcal{MAX}\)-\(\mathcal{MIN}\) ant system. Future Generation Computer Systems 16(8), 889–914 (2000)
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)
Hettich, S., Bay, S.D.: The uci kdd archive (1996), http://kdd.ics.uci.edu
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Martens, D., De Backer, M., Haesen, R., Baesens, B., Mues, C., Vanthienen, J. (2006). Ant-Based Approach to the Knowledge Fusion Problem. In: Dorigo, M., Gambardella, L.M., Birattari, M., Martinoli, A., Poli, R., Stützle, T. (eds) Ant Colony Optimization and Swarm Intelligence. ANTS 2006. Lecture Notes in Computer Science, vol 4150. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11839088_8
Download citation
DOI: https://doi.org/10.1007/11839088_8
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-38482-3
Online ISBN: 978-3-540-38483-0
eBook Packages: Computer ScienceComputer Science (R0)