Proceedings of the 2015 5th International Conference on Computer Sciences and Automation Engineering

One Bayesian Network Construction Algorithm Based On Dimensionality Reduction

Authors
Shuo Quan, Pengfei Sun, Guoshi Wu, Jie Hu
Corresponding Author
Shuo Quan
Available Online February 2016.
DOI
10.2991/iccsae-15.2016.43How to use a DOI?
Keywords
feature clustering, feature mapping, Bayesian network, dimensionality reduction
Abstract

Bayesian network is a common probabilistic graphical model. It often can achieve good result in expression of uncertainty for knowledge and regular as well as in data classification. Since Bayesian network construction is a relatively complex problem, we propose a Bayesian network construction dimensionality reduction algorithm (BNDR). This algorithm maps a set of associated features to an abstract feature node by feature clustering and mapping. It can ensure no loss in accuracy and improve the time efficiency. For more complex industrial scenario, using the BNDR, you can get better practical efficiency.

Copyright
© 2016, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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Volume Title
Proceedings of the 2015 5th International Conference on Computer Sciences and Automation Engineering
Series
Advances in Computer Science Research
Publication Date
February 2016
ISBN
978-94-6252-156-8
ISSN
2352-538X
DOI
10.2991/iccsae-15.2016.43How to use a DOI?
Copyright
© 2016, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Cite this article

TY  - CONF
AU  - Shuo Quan
AU  - Pengfei Sun
AU  - Guoshi Wu
AU  - Jie Hu
PY  - 2016/02
DA  - 2016/02
TI  - One Bayesian Network Construction Algorithm Based On Dimensionality Reduction
BT  - Proceedings of the 2015 5th International Conference on Computer Sciences and Automation Engineering
PB  - Atlantis Press
SP  - 222
EP  - 229
SN  - 2352-538X
UR  - https://doi.org/10.2991/iccsae-15.2016.43
DO  - 10.2991/iccsae-15.2016.43
ID  - Quan2016/02
ER  -