Abstract
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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Acknowledgments
The research reported here was supported by the National Natural Science Foundation of China under contract number NSFC61572279.
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Luo, G., Zhao, B. & Du, S. Causal inference and Bayesian network structure learning from nominal data. Appl Intell 49, 253–264 (2019). https://doi.org/10.1007/s10489-018-1274-3
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DOI: https://doi.org/10.1007/s10489-018-1274-3