摘要
Ordering based search methods have advantages over graph based search methods for structure learning of Bayesian networks in terms on the efficiency. With the aim of further increasing the accuracy of ordering based search methods, we first propose to increase the search space, which can facilitate escaping from the local optima. We present our search operators with majorizations, which are easy to implement. Experiments show that the proposed algorithm can obtain significantly more accurate results. With regard to the problem of the decrease on efficiency due to the increase of the search space, we then propose to add path priors as constraints into the swap process. We analyze the coefficient which may influence the performance of the proposed algorithm, the experiments show that the constraints can enhance the efficiency greatly, while has little effect on the accuracy. The final experiments show that, compared to other competitive methods, the proposed algorithm can find better solutions while holding high efficiency at the same time on both synthetic and real data sets.
Ordering based search methods have advantages over graph based search methods for structure learning of Bayesian networks in terms on the efficiency. With the aim of further increasing the accuracy of ordering based search methods, we first propose to increase the search space, which can facilitate escaping from the local optima. We present our search operators with majorizations, which are easy to implement. Experiments show that the proposed algorithm can obtain significantly more accurate results. With regard to the problem of the decrease on efficiency due to the increase of the search space, we then propose to add path priors as constraints into the swap process. We analyze the coefficient which may influence the performance of the proposed algorithm, the experiments show that the constraints can enhance the efficiency greatly, while has little effect on the accuracy. The final experiments show that, compared to other competitive methods, the proposed algorithm can find better solutions while holding high efficiency at the same time on both synthetic and real data sets.
基金
supported by the National Natural Science Fundation of China(61573285)
the Doctoral Fundation of China(2013ZC53037)
作者简介
HE Chuchao was born in 1992.He is a Ph.D.candidate in the Department of System Engineering,Northwestern Polytechnical University.His research interests focus on Bayesian network learning,especially on structure learning.E-mail:xomrssh@163.com;Corresponding author:GAO Xiaoguang was born in 1957.She received her Ph.D.degree from Northwestern Polytechnical University in 1989.Her research interests are machine learning theory,Bayesian network theory,and multi-agent control application.E-mail:cxg2012@nwpu.edu.cn;GUO Zhigao was born in 1987.He is a Ph.D.candidate in the Department of System Engineering,Northwestern Polytechnical University.His research interests focus on Bayesian network learning,especially learning with domain knowledge.E-mail:guozhigao2004@163.com
