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Reseach Article

Online Decision Support System and Machine Learning Modeling using Bayesian Belief Network

by S. Karpagaselvi, M. Thiyagarajan
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 44 - Number 1
Year of Publication: 2012
Authors: S. Karpagaselvi, M. Thiyagarajan
10.5120/6231-8335

S. Karpagaselvi, M. Thiyagarajan . Online Decision Support System and Machine Learning Modeling using Bayesian Belief Network. International Journal of Computer Applications. 44, 1 ( April 2012), 34-36. DOI=10.5120/6231-8335

@article{ 10.5120/6231-8335,
author = { S. Karpagaselvi, M. Thiyagarajan },
title = { Online Decision Support System and Machine Learning Modeling using Bayesian Belief Network },
journal = { International Journal of Computer Applications },
issue_date = { April 2012 },
volume = { 44 },
number = { 1 },
month = { April },
year = { 2012 },
issn = { 0975-8887 },
pages = { 34-36 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume44/number1/6231-8335/ },
doi = { 10.5120/6231-8335 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:34:28.086244+05:30
%A S. Karpagaselvi
%A M. Thiyagarajan
%T Online Decision Support System and Machine Learning Modeling using Bayesian Belief Network
%J International Journal of Computer Applications
%@ 0975-8887
%V 44
%N 1
%P 34-36
%D 2012
%I Foundation of Computer Science (FCS), NY, USA
Abstract

In this study we investigate the use of Bayesian Belief Networks (BBN) for developing a practical framework for machine learning process incorporating the commonsense reasoning. Bayesian Belief Networks grant a systematic and localized method for structuring probabilistic information about a situation into coherent whole. Bayesian networks have been established as a ubiquitous tool for modelling and reasoning under uncertainty. In this study we attempt to develop a graphical model that is used to represent knowledge about the uncertain domain in which the nodes are the random variables and the edges between the nodes represent probabilistic dependencies among the corresponding random variables. These conditional dependencies in the graph are often estimated by using some known statistical and computational methods. Thus developed Bayesian belief network along with joint probability distribution in the factored form can be used to evaluate all possible inference queries both predictive and diagnostic by marginalization. We experimentally developed a model for educational institutions which could be used to take decision on considering various factors link campus placement, total cost per year, academic excellence etc.

References
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Index Terms

Computer Science
Information Sciences

Keywords

Bayesian Belief Network Marginalization Joint Probability Conditional Probability Machine Learning Node Probability Table