Using Evolutionary Programming and Minimum Description Length Principle for Data Mining of Bayesian Networks
Issue No. 02 - February (1999 vol. 21)
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/34.748825
<p><b>Abstract</b>—We have developed a new approach (MDLEP) to learning Bayesian network structures based on the Minimum Description Length (MDL) principle and Evolutionary Programming (EP). It employs a MDL metric, which is founded on information theory, and integrates a knowledge-guided genetic operator for the optimization in the search process.</p>
Evolutionary computation, Bayesian networks, unsupervised learning, minimum description length principle, genetic algorithms.
M. L. Wong, K. S. Leung and W. Lam, "Using Evolutionary Programming and Minimum Description Length Principle for Data Mining of Bayesian Networks," in IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 21, no. , pp. 174-178, 1999.