Title

Using cooperative coevolution for data mining of Bayesian networks

Document Type

Book chapter

Source Publication

Advanced techniques in knowledge discovery and data mining

Publication Date

2005

First Page

153

Last Page

175

Publisher

Springer

Abstract

Bayesian networks are formal knowledge representation tools that provide reasoning under uncertainty. The applications of Bayesian networks are widespread, including data mining, information retrieval, and various diagnostic systems. Although Bayesian networks are useful, the learning problem, namely to construct a network automatically from data, remains a difficult problem. Recently, some researchers have adopted evolutionary computation for learning. However, the drawback is that the approach is slow. In this chapter, we propose a hybrid framework for Bayesian network learning. By combining the merits of two different learning approaches, we expect an improvement in learning speed. In brief, the new learning algorithm consists of two phases: the conditional independence (CI) test phase and the search phase. In the CI test phase, we conduct dependency analysis, which helps to reduce the search space. In the search phase, we perform model searching using an evolutionary approach, called cooperative coevolution. When comparing our new algorithm with an existing algorithm, we find that our algorithm performs faster and is more accurate in many cases.

DOI

10.1007/1-84628-183-0_6

Additional Information

ISBN of the source publication: 1852338679

Full-text Version

Publisher’s Version

Recommended Citation

Wong, M. L., Lee, S. Y., & Leung, K. S. (2005). Using cooperative coevolution for data mining of Bayesian networks. In N. R. Pal & L. Jain (Eds.), Advanced techniques in knowledge discovery and data mining (pp.153-175). doi: 10.1007/1-84628-183-0_6