期刊
APPLIED INTELLIGENCE
卷 49, 期 1, 页码 253-264出版社
SPRINGER
DOI: 10.1007/s10489-018-1274-3
关键词
Causal inference; Bayesian network; Discrete causal method; Nominal data; Alarm analysis
资金
- National Natural Science Foundation of China [NSFC61572279]
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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