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您的检索式：作者名="HE Chuchao"
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| 1 | Structure learning on Bayesian networks by finding the optimal ordering with and without priors显示文摘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. | HE Chuchao GAO Xiaoguang GUO Zhigao | 2018 | Journal of Systems Engineering and Electronics2018，29，6： | 5 |
| 2 | MMOS+ Ordering Search Method for Bayesian Network Structure Learning and Its Application显示文摘To address the problem of a reduced efficiency due to an increase of the search space,it has been proposed that priors could be added as constraints to the OS+algorithm,which are Parent and children(PC)sets of each node obtained using the Max-min parent and children(MMPC)algorithm.Experimentai results indicate that compared to other competitive met hods,the proposed algorithm yields better solutions while maintaining high efficiency.Bayesian network(BN)sensitivity analysis is also proposed,which allows the network structure to be det ermined via a proposed ordering search met hod.We performed sensitivity analysis to det ermine the accuracy of the airborne avionics system,for which a simulation model is cons true ted to generate data samples,and the main effect of each error index is obtained using different sensitivity analysis met hods.Experimen tai resul ts indicate that the proposed BN method produces more accurate results when there is insufficient sample data,and this method can elucidate causal relationships that are present in the data. | HE Chuchao GAO Xiaoguang WAN Kaifang | 2020 | Chinese Journal of Electronics2020，29，1： | 3 |
| 3 | Causal constraint pruning for exact learning of Bayesian network structure显示文摘How to improve the efficiency of exact learning of the Bayesian network structure is a challenging issue.In this paper,four different causal constraints algorithms are added into score calculations to prune possible parent sets,improving state-ofthe-art learning algorithms’efficiency.Experimental results indicate that exact learning algorithms can significantly improve the efficiency with only a slight loss of accuracy.Under causal constraints,these exact learning algorithms can prune about 70%possible parent sets and reduce about 60%running time while only losing no more than 2%accuracy on average.Additionally,with sufficient samples,exact learning algorithms with causal constraints can also obtain the optimal network.In general,adding max-min parents and children constraints has better results in terms of efficiency and accuracy among these four causal constraints algorithms. | TAN Xiangyuan GAO Xiaoguang HE Chuchao WANG Zidong | 2021 | Journal of Systems Engineering and Electronics2021，32，4： | 0 |
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