NDLI: Constructing dependence ordering for B&B technique in learning Bayesian belief network

Content Provider	IEEE Xplore Digital Library
Author	Wei Gao Kun Niu
Copyright Year	2011
Description	Author affiliation: School of Software Engineering, Beijing University of Posts and Telecoms, 100029, China (Wei Gao; Kun Niu)
Abstract	The data mining technology is more and more widely used. How to construct a Bayesian belief network has been discussed in many different ways. As one of the classical algorithms, the branch and bound technique based on the minimum description length principle has been proposed by Joe Suzuki in 1998. But one of the most important premises of the B&B Technique is that an attributes' dependence ordering has been prepared. To address the problem, a new method is proposed for attributes' dependence ordering. The algorithm first constructs a dependence tree using training dataset, then we use breadth first searching and get the dependence ordering. That results in a raw ordering. Then we order the nodes that are in the same layer of the dependence tree in order to make the result more accurate. This paper uses real datasets from the telecom industry as the test datasets. The result shows that the algorithm can construct the dependence ordering with good performance.
Starting Page	2886
Ending Page	2889
File Size	321164
Page Count	4
File Format	PDF
ISBN	9781457705359
e-ISBN	9781457705366
DOI	10.1109/AIMSEC.2011.6010807
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-08-08
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Training Accuracy Bayesian methods Dependence ordering Dependence tree Training data Data structures Breadth first searching Complexity theory B&B technique Mutual information Bayesian belief network
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

An improved B&B technique applying to telecom industry

New algorithm for constructing Bayesian network structures from data

A Smart Design for the TAN Classifier

Wait-Free Primitives for Initializing Bayesian Network Structure Learning on Multicore Processors

A Clustering Based Bayesian Network Classifier

The bayesian approach to belief propagation in digital ecosystems

Two evolutionary methods for learning Bayesian network structures

Learning Belief Networks from Data: An Information Theory Based Approach (0)

Efficient learning of Bayesian network classifiers: An

Constructing dependence ordering for B&B technique in learning Bayesian belief network

Similar Documents

An improved B&B technique applying to telecom industry

New algorithm for constructing Bayesian network structures from data

A Smart Design for the TAN Classifier

Wait-Free Primitives for Initializing Bayesian Network Structure Learning on Multicore Processors

A Clustering Based Bayesian Network Classifier

The bayesian approach to belief propagation in digital ecosystems

Two evolutionary methods for learning Bayesian network structures

Learning Belief Networks from Data: An Information Theory Based Approach (0)

Efficient learning of Bayesian network classifiers: An

Constructing dependence ordering for B&B technique in learning Bayesian belief network