This paper describes a novel data mining approach that employs evolutionary programming to discover knowledge represented in Bayesian networks. There are two different approaches to the network learning problem. The first one uses dependency analysis, while the second one searches good network structures according to a metric. Unfortunately, both approaches have their own drawbacks. Thus, we propose a novel hybrid algorithm of the two approaches, which consists of two phases, namely, the Conditional Independence (CI) test and the search phases. A new operator is introduced to further enhance the search efficiency. We conduct a number of experiments and compare the hybrid algorithm with our previous algorithm, MDLEP , which uses EP for network learning. The empirical results illustrate that the new approach has better performance. We apply the approach to a data sets of direct marketing and compare the performance of the evolved Bayesian networks obtained by the new algorithm with the models generated by other methods. In the comparison, the induced Bayesian networks produced by the new algorithm outperform the other models.
|Title of host publication||Proceedings - IEEE International Conference on Data Mining, ICDM|
|Number of pages||8|
|Publication status||Published - 1 Jan 2002|
Bibliographical notePaper presented at the 2nd IEEE International Conference on Data Mining, Dec 09-12, 2002, Maebashi City, Japan. ISBN of the source publication: 9780769517544
WONG, M. L., LEE, S. Y., & LEUNG, K. S. (2002). A hybrid approach to discover Bayesian networks from databases using evolutionary programming. In Proceedings - IEEE International Conference on Data Mining, ICDM (pp. 498-505) https://doi.org/10.1109/ICDM.2002.1183994