Research And Application On Learning & Decision Methods Based On Bayesian Network

The retrieval of new knowledge, rule & decision support information from large scale data in network environment is a hot research point during information time. It focuses on studying the knowledge discovering methods of efficiently analyzing and mining large scale data. Bayesian Network (BN), combined with the knowledge of graph theory and statistics, prompts a nature method to express casual relation. It can be used to express complex uncertainty of random variables and discovery latent relation among data. Therefore, Bayesian Network has an important research value and wide application foreground in KDD domain.This dissertation has study learning & decision methods based on Bayesian Network basic theory, which takes science and technology plan program of ChongQing (CSTC 2006AA7024)"Research on Water Environment Security Early Warning Platform and Key Technology of Science Decision in Three Gorge Region"as application background. Aiming at the problem of data intelligent learning, focusing on Bayesian network learning, Bayesian Network classifiers learning, Probability Relation Model learning and multi-agent group decision support system based BN, the main contributions of this dissertation are as follow:â‘ It analyzes the modeling flow of Bayesian Network and focuses on learning Bayesian Network structure from data. According to analyzing classical Structure Learning methods (K2 and MCMC algorithms), an improved Bayesian Networks Structure Learning algorithm is proposed which combined with the merits of above two algorithms and the idea of model averaging. Experiment results show that the proposed algorithm can cover shortages of K2 and MCMC algorithms and can quickly achieve a comparative correct and steady model structure without priori knowledge. The algorithm has better robust which can be application in variables correlation analysis and modeling of large scale data for science decision.â‘¡A learning algorithm for modeling hiberarchy Na?ve Bayesian classifier SAHNB is proposed which learns hiberarchy relations between variables by aggregating variables. It firstly uses Conditional Mutual Information between variables to derermine the scale of latent aggregate nodes, then usee Simulation Anneal algorithm to search the classifier with higher score. Experiment results show that SAHNB has better classification effct comparing with NB, TAN &GBN-SA. Moreover, the nodes adding in model can aggregate the states of attribute nodes and explain semantic in order to supply correlation classification rules. SAHNB classifier has been applied in water quality security early warning system and received better results.â‘¢According to the fact problem, it proposes a construct method of risk forecasting model for water eutrophication combined with the merits of Probability Relation Model. This is a new research taste in the domain. The construct flow is as follow: firstly it sets candidate parent nodes for each correlation variable by expert knowledge; then uses SQL language to process candidate parent node sets, including in multi-set operation, aggregate operation and discretization, in order to relax computation complex of heuristic search-score algorithms; finally, it learns the model structure by the similar K2 algorithm and divide the model by classified indicators. This model has received the first step result on application research.â‘£It has research on modeling methods combining MAS with Byaesian Network. Based on Bayesian Network and Utility Theory, it designs the single agent structure and studies a bidirectional learning mechanical with feedback control. Then it designs a hiberarchy structure of multi-agent group decision support system and a dynamics organization method faced on decision tasks. Finally, it defines a multi-agent negotiation model and proposes a negotiation algorithm based on Bayesian belief model for conflict, emphasis and persuading.In conclusion, above works can provide efficacious data mining and knowledge discovering methods for science decision. Moreover, it's tested that these methods are feasible in applitions.