One Bayesian Network Construction Algorithm Based On Dimensionality Reduction

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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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  -