Robust inference of Bayesian networks using speciated evolution and ensemble

Abstract

Recently, there are many researchers to design Bayesian network structures using evolutionary algorithms but most of them use the only one fittest solution in the last generation. Because it is difficult to integrate the important factors into a single evaluation function, the best solution is often biased and less adaptive. In this paper, we present a method of generating diverse Bayesian network structures through fitness sharing and combining them by Bayesian method for adaptive inference. In the experiments with Asia network, the proposed method provides with better robustness for handling uncertainty owing to the complicated redundancy with speciated evolution. © Springer-Verlag Berlin Heidelberg 2005.