Ant-based approach to the knowledge fusion problem

Martens, David, De Backer, Manu, Haesen, Raf, Baesens, Bart, Mues, Christophe and Vanthienen, Jan (2006) Ant-based approach to the knowledge fusion problem. In, Ant Colony Optimization and Swarm Intelligence. (Lecture Notes in Computer Science, 4150/2006) 5th International Workshop on Ant Colony Optimization and Swarm Intelligence (ANTS2006) (04/09/06 - 07/09/06) Berlin, Germany. Springer, pp. 84-95. (doi:10.1007/11839088_8).

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.

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Contributors

Author: Bart Baesens

Author: Christophe Mues

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