Causal inference and Bayesian network structure learning from nominal data

This study investigates a discrete causal method for nominal data (DCMND) which is one of the important issues of causal inference. It is utilized to learn the causal Bayesian network to reflect the interconnections between variables in our paper. This article also proposes a Bayesian network constr...

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Bibliographic Details
Published in:Applied intelligence (Dordrecht, Netherlands) Vol. 49; no. 1; pp. 253 - 264
Main Authors: Luo, Guiming, Zhao, Boxu, Du, Shiyuan
Format: Journal Article
Language:English
Published: New York Springer US 01.01.2019
Springer Nature B.V
Subjects:
Algorithms
Artificial Intelligence
Bayesian analysis
Communications systems
Computer Science
Inference
Machine learning
Machines
Manufacturing
Mechanical Engineering
Mobile communication systems
Processes
Causal inference
Alarm analysis
Discrete causal method
Nominal data
Bayesian network
ISSN:0924-669X, 1573-7497
Online Access:Get full text
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Summary:This study investigates a discrete causal method for nominal data (DCMND) which is one of the important issues of causal inference. It is utilized to learn the causal Bayesian network to reflect the interconnections between variables in our paper. This article also proposes a Bayesian network construction algorithm based on discrete causal inference (BDCI) and an extended BDCI Bayesian network construction algorithm based on DCMND. Furthermore, the paper studies the alarm data of mobile communication system in practice. The results suggest that decision criterion based our method is effective in causal inference and the Bayesian network constructed by our method has better classification accuracy compared to other methods.
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ISSN:0924-669X
1573-7497
DOI:10.1007/s10489-018-1274-3