Learning functional dependency networks based on genetic programming
Proceedings of the 5th IEEE International Conference on Data Mining, ICDM 2005
IEEE Computer Society
Bayesian Network (BN) is a powerful network model, which represents a set of variables in the domain and provides the probabilistic relationships among them. But BN can handle discrete values only; it cannot handle continuous, interval and ordinal ones, which must be converted to discrete values and the order information is lost. Thus, BN tends to have higher network complexity and lower understandability. In this paper, we present a novel dependency network which can handle discrete, continuous, interval and ordinal values through functions; it has lower network complexity and stronger expressive power; it can represent any kind of relationships; and it can incorporate a-priori knowledge though user-defined functions. We also propose a novel Genetic Programming (GP) to learn dependency networks. The novel GP does not use any knowledge-guided nor application-oriented operator, thus it is robust and easy to replicate. The experimental results demonstrate that the novel GP can successfully discover the target novel dependency networks, which have the highest accuracy and the lowest network complexity.
Copyright © 2005 IEEE. Access to external full text or publisher's version may require subscription.
Paper presented at the 5th IEEE International Conference on Data Mining (ICDM 2005), 27-30 November 2005, Houston, Texas.
ISBN of the source publication: 9780769522784
Shum, W.-H., Leung, K.-S., & Wong, M.-L. (2005). Learning functional dependency networks based on genetic programming. In Proceedings of the 5th IEEE International Conference on Data Mining, ICDM 2005 (pp. 394-401). Los Alamitos: IEEE Computer Society. doi: 10.1109/ICDM.2005.86