Identification of hidden disaster causing factors in coal mine based on Naive Bayes algorithm

In order to overcome the problems of low recognition rate and long recognition time existing in traditional methods, a method for identifying hidden disaster factors in coal mines based on Naive Bayes algorithm was proposed. The posterior probability of Bayesian network is calculated to obtain the m...

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Veröffentlicht in:Journal of intelligent & fuzzy systems 2021-01, Vol.41 (2), p.2823-2831
Hauptverfasser: Zhao, Yifan, Tian, Shuicheng
Format: Artikel
Sprache:eng
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Zusammenfassung:In order to overcome the problems of low recognition rate and long recognition time existing in traditional methods, a method for identifying hidden disaster factors in coal mines based on Naive Bayes algorithm was proposed. The posterior probability of Bayesian network is calculated to obtain the maximum value of the posterior probability, so as to judge the categories of hidden disaster factors in coal mines. The method of combining soft and hard threshold functions is used to denoise Naive Bayes network. Combined with the structural equation of coal mine concealed disaster-causing factors, the index weight of coal mine disaster-causing factors is calculated, and a fast identification model of disaster-causing factors is built to complete the identification. Experimental results show that the quality factors of the proposed method are all higher than 8, the recognition rate is as high as 98%, and the recognition time is basically controlled within 0.8 s.
ISSN:1064-1246
1875-8967
DOI:10.3233/JIFS-202726