Structure learning on Bayesian networks by finding the optimal ordering with and without priors

Chuchao He, Xiaoguang Gao, Zhigao Guo

科研成果: 期刊稿件文章同行评审

4 引用 (Scopus)

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.

源语言英语
文章编号8599103
页(从-至)1209-1227
页数19
期刊Journal of Systems Engineering and Electronics
29
6
DOI
出版状态已出版 - 12月 2018

指纹

探究 'Structure learning on Bayesian networks by finding the optimal ordering with and without priors' 的科研主题。它们共同构成独一无二的指纹。

引用此