A Model Selection Method of Influence Diagrams Based on PS-EM Algorithm and BP Neural Network
Yao Hong · 2007
In the model selection of influence diagrams(IDs),the problems of the data dependency,the computation complexity and non-probability relation are discussed.Based on the structure decomposition of IDs,a PS-EM algorithm is presented.A BP Neural Network is introduced by learning local utility function of each utility node,and the overfitting is avoided by inducing the threshold of weights.To reduce the data dependency,a new MDL scoring is presented which includes the prior knowledge of network structures.Based on SEM algorithm,PS-EM algorithm induces the new MDL scoring,and separates parameters learning from structures scoring to improve the computation efficiency.Compared with SEM algorithm,the performances of both the computation complexity and the data dependency of PS-EM algorithm are improved,and the model selection of the utility part is easy to achieve.