The battlefield situation changes rapidly because underwater targets'are concealment and the sea environment is uncertain.So,a great number of situation information greatly increase,which need to be dealt with in ...The battlefield situation changes rapidly because underwater targets'are concealment and the sea environment is uncertain.So,a great number of situation information greatly increase,which need to be dealt with in the course of scouting underwater targets.Situation assessment in sea battlefield with a lot of uncertain information is studied,and a new situation assessment method of scouting underwater targets with fixed-wing patrol aircraft is proposed based on the cloud Bayesian network,which overcomes the deficiency of the single cloud model in reasoning ability and the defect of Bayesian network in knowledge representation.Moreover,in the method,the cloud model knowledge deal with the input data of Bayesian network reasoning,and the advantages in knowledge representation of cloud theory and reasoning of Bayesian network are applied;also,the fuzziness and stochasticity of cloud theory in knowledge expression,the reasoning ability of Bayesian network,are combined.Then,the situation assessment model of scouting underwater targets with fixed-wing patrol aircraft is established.Hence,the directed acyclic graph of Bayesian network structure is constructed and the assessment index is determined.Next,the cloud model is used to deal with Bayesian network,and the discrete Bayesian network is obtained.Moreover,after CPT of each node and the transformation between certainty degree and probability are accomplished;the final situation level is obtained through a probability synthesis formula.Therefore,the target type and the operational intention of the other side are deduced to form the battlefield situation.Finally,simulations are carried out,and the rationality and validity of the proposed method are testified by simulation results.By this method,the battlefield situation can be gained.And this method has a wider application range,especially for large sample data processing,and it has better practicability.展开更多
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 p...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.展开更多
为了及早发现重症监护室中的急性肾损伤高危患者,为其提供适当的护理,实现医疗资源的合理利用,研究建立因果贝叶斯网络模型进行急性肾损伤高危患者死亡风险预测。从重症监护医学信息市场(Medical Information Mart for Intensive CareⅢ...为了及早发现重症监护室中的急性肾损伤高危患者,为其提供适当的护理,实现医疗资源的合理利用,研究建立因果贝叶斯网络模型进行急性肾损伤高危患者死亡风险预测。从重症监护医学信息市场(Medical Information Mart for Intensive CareⅢ,MIMIC-Ⅲ)数据库中筛选了25个研究变量和3870条患者数据,使用因果发现算法进行特征降维。通过NO TEARS算法构建因果图并建立因果贝叶斯网络进行实验,通过机器学习算法验证重要特征的合理性,并对网络结构进行因果效应估计,模型具有最高的受试者工作特征曲线下面积(Area Under the Receiver Operating Characteristic,AUROC)分数,为81.7%,优于逻辑回归(Logistic Regression,LR)、随机森林(Random Forest,RF)和极端梯度提升树(eXtreme Gradient Boosting,XGBoost)。此外,模型的重要特征预测能力在各种建模中都很稳健,构建的因果贝叶斯网络具有更好的预测效果并具备良好的解释能力。展开更多
We investigated the application of Causal Bayesian Networks (CBNs) to large data sets in order to predict user intent via internet search prediction. Here, sample data are taken from search engine logs (Excite, Altavi...We investigated the application of Causal Bayesian Networks (CBNs) to large data sets in order to predict user intent via internet search prediction. Here, sample data are taken from search engine logs (Excite, Altavista, and Alltheweb). These logs are parsed and sorted in order to create a data structure that was used to build a CBN. This network is used to predict the next term or terms that the user may be about to search (type). We looked at the application of CBNs, compared with Naive Bays and Bays Net classifiers on very large datasets. To simulate our proposed results, we took a small sample of search data logs to predict intentional query typing. Additionally, problems that arise with the use of such a data structure are addressed individually along with the solutions used and their prediction accuracy and sensitivity.展开更多
基金Natural Science Foundation of Shangdong,Grant/Award Number:ZR2019MF065.
文摘The battlefield situation changes rapidly because underwater targets'are concealment and the sea environment is uncertain.So,a great number of situation information greatly increase,which need to be dealt with in the course of scouting underwater targets.Situation assessment in sea battlefield with a lot of uncertain information is studied,and a new situation assessment method of scouting underwater targets with fixed-wing patrol aircraft is proposed based on the cloud Bayesian network,which overcomes the deficiency of the single cloud model in reasoning ability and the defect of Bayesian network in knowledge representation.Moreover,in the method,the cloud model knowledge deal with the input data of Bayesian network reasoning,and the advantages in knowledge representation of cloud theory and reasoning of Bayesian network are applied;also,the fuzziness and stochasticity of cloud theory in knowledge expression,the reasoning ability of Bayesian network,are combined.Then,the situation assessment model of scouting underwater targets with fixed-wing patrol aircraft is established.Hence,the directed acyclic graph of Bayesian network structure is constructed and the assessment index is determined.Next,the cloud model is used to deal with Bayesian network,and the discrete Bayesian network is obtained.Moreover,after CPT of each node and the transformation between certainty degree and probability are accomplished;the final situation level is obtained through a probability synthesis formula.Therefore,the target type and the operational intention of the other side are deduced to form the battlefield situation.Finally,simulations are carried out,and the rationality and validity of the proposed method are testified by simulation results.By this method,the battlefield situation can be gained.And this method has a wider application range,especially for large sample data processing,and it has better practicability.
基金supported by the National Natural Science Foundation of China(61573285).
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.
文摘为了及早发现重症监护室中的急性肾损伤高危患者,为其提供适当的护理,实现医疗资源的合理利用,研究建立因果贝叶斯网络模型进行急性肾损伤高危患者死亡风险预测。从重症监护医学信息市场(Medical Information Mart for Intensive CareⅢ,MIMIC-Ⅲ)数据库中筛选了25个研究变量和3870条患者数据,使用因果发现算法进行特征降维。通过NO TEARS算法构建因果图并建立因果贝叶斯网络进行实验,通过机器学习算法验证重要特征的合理性,并对网络结构进行因果效应估计,模型具有最高的受试者工作特征曲线下面积(Area Under the Receiver Operating Characteristic,AUROC)分数,为81.7%,优于逻辑回归(Logistic Regression,LR)、随机森林(Random Forest,RF)和极端梯度提升树(eXtreme Gradient Boosting,XGBoost)。此外,模型的重要特征预测能力在各种建模中都很稳健,构建的因果贝叶斯网络具有更好的预测效果并具备良好的解释能力。
文摘We investigated the application of Causal Bayesian Networks (CBNs) to large data sets in order to predict user intent via internet search prediction. Here, sample data are taken from search engine logs (Excite, Altavista, and Alltheweb). These logs are parsed and sorted in order to create a data structure that was used to build a CBN. This network is used to predict the next term or terms that the user may be about to search (type). We looked at the application of CBNs, compared with Naive Bays and Bays Net classifiers on very large datasets. To simulate our proposed results, we took a small sample of search data logs to predict intentional query typing. Additionally, problems that arise with the use of such a data structure are addressed individually along with the solutions used and their prediction accuracy and sensitivity.
