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Research and Implement of Structure Learning Algorithm for Hybrid Bayesian Networks

Author: YanPengFei
Tutor: DongLiYan
School: Jilin University
Course: Computer Software and Theory
Keywords: Data Mining Bayesian networks Node sequence Pseudo - Bayesian network Minimum d- separation set
CLC: TP183
Type: Master's thesis
Year: 2010
Downloads: 156
Quote: 0
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Abstract


The goal of data mining is a useful and easy - to - understand information discovery rules, implicit in the massive data mode or correlation , integrated multidisciplinary field knowledge , roughly five categories of tasks to be completed , that prediction , classification, clustering association rules and time series . This article will focus on the structure learning of Bayesian network classifiers . Bayesian network structure learning process often requires experts in the field to provide some a priori information to reduce the network search space , making the classification accuracy of the resulting network model is heavily dependent on the knowledge of experts in the field . To address these deficiencies , we propose a hybrid structure learning algorithm HBN , the algorithm is a three-stage algorithm and does not require any a priori completely from the data set to learn a Bayesian network classification model . The HBN algorithm was first introduced in the pseudo - Bayesian network structure thinking , in the structure of the learning process , we have designed a new scoring function , is used to measure the network model and the data model differences in the expression of marginal independence and the first-order conditional independence level. The process of closed networks , we also consider the node probability categorical attributes and data attributes directly dependent , which makes HBN model combines the advantages of the NBC model and the BN model , while avoiding their disadvantages . In addition, we also find the approximate minimum set of d- separation algorithm is presented from the perspective of the implementation of efficiency improvement . A large number of experiments show that , the HBN model compared to the the NBC model and TAN model in classification accuracy has been greatly improved . Inadequacies HBN algorithm uses a greedy search strategies to find a sub-optimal network structure , the algorithm is more suited to construct a network of small and medium-sized structure .

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