NDLI: An incremental structure learning approach for Bayesian Network

Content Provider	IEEE Xplore Digital Library
Author	Shuohao Li Jun Zhang Boliang Sun Jun Lei
Copyright Year	2014
Description	Author affiliation: Coll. of Inf. Syst. & Manage., Nat. Univ. of Defense Technol., Changsha, China (Shuohao Li; Jun Zhang; Boliang Sun; Jun Lei)
Abstract	Structure learning of Bayesian Network (BN) is one of important topics in machine learning and widely applied in expert system. The traditional algorithms for structure learning are usually focused on the batch data in nature. It is difficult to learn the structure quickly from the huge amounts of data. But in many practical applications, the structure of BN should be learned by using time-series data that are available to us. To achieve this goal, we propose an incremental structure learning approach for BN. Firstly, we proposed the framework of incremental structure learning and a new evaluation criterion “ABIC” (Adopt Bayesian Information Criterion) based on the BIC. Then, three phase algorithm is used to learn the structure. Numerical experiments on two standard networks show that our proposed algorithm can greatly improve the accuracy of the structure and the total of learning time is greatly reduced.
Starting Page	4817
Ending Page	4822
File Size	6707045
Page Count	6
File Format	PDF
ISBN	9781479937073
e-ISBN	9781479937080
DOI	10.1109/CCDC.2014.6853036
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-05-31
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Algorithm design and analysis Incremental Structure Learning Accuracy ABIC Insurance Bayesian Network Genetics Probability distribution Three phase algorithm Bayes methods Equations
Content Type	Text
Resource Type	Article

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