@inproceedings{9f8157de9eae4cc2acd2c6b0541524ef,
title = "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.",
author = "Kim, {Kyung Joong} and Yoo, {Ji Oh} and Cho, {Sung Bae}",
year = "2005",
doi = "10.1007/11425274_10",
language = "English",
isbn = "3540258787",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "92--101",
booktitle = "Foundations of Intelligent Systems - 15th International Symposium, ISMIS 2005, Proceedings",
address = "Germany",
note = "15th International Symposium on Methodologies for Intelligent Systems, ISMIS 2005 ; Conference date: 25-05-2005 Through 28-05-2005",
}