Reliability parameter selection is very important in the period of equipment project design and demon-
stration. In this paper, the problem in selecting the reliability parameters and their number is proposed. In order
to solve this problem, the thought of text mining is used to extract the feature and curtail feature sets from text
data ˉrstly, and frequent pattern tree (FPT) of the text data is constructed to reason frequent item-set between
the key factors by frequent patter growth (FPG) algorithm. Then on the basis of fuzzy Bayesian network (FBN)
and sample distribution, this paper fuzziˉes the key attributes, which forms associated relationship in frequent
item-sets and their main parameters, eliminates the subjective in°uence factors and obtains condition mutual
information and maximum weight directed tree among all the attribute variables. Furthermore, the hybrid model
is established by reason fuzzy prior probability and contingent probability and concluding parameter learning
method. Finally, the example indicates the model is believable and e?ective.
SHUAI Yong1;2 (帅勇), SONG Tailiang3 (宋太亮), WANG Jianping1 (王建平), ZHAN Wenbin4 (詹文斌)
. Hybrid Reliability Parameter Selection Method Based on Text Mining, Frequent Pattern Growth algorithm and Fuzzy Bayesian Network[J]. Journal of Shanghai Jiaotong University(Science), 2018
, 23(3)
: 423
.
DOI: 10.1007/s12204-018-1945-6
[1] ZHEN X, LI J D. Established method study for super-parameter of prior distribution in reliability test [J].Electronic Product Reliability and Environmental Test-ing, 2013, 31(5): 14-16 (in Chinese).
[2] ZHOU H, ZHU Y L. Research on selection and deter-mination of RMS parameters of military engineer ma-chinery [J]. Engineer Equipment Research, 2011, 30(1):53-57 (in Chinese).
[3] LU X Q, JIN L. Selection of heat-setting parameters ofmechanical products with DOE method [J]. Materialsand Heat Treatment Technology, 2012, 41(16): 216-217(in Chinese).
[4] SHAO L C, ZHU J G, JI J C, et al. Reliability parameters' selection when a decoy erecting [J]. EngineerEquipment Research, 2011, 30(5): 9-12 (in Chinese).
[5] SHUBHADA P N, RAJENDRA K. Text mining [J].Library Review, 2015, 64(3): 248-268.
[6] LIAO Q, HAO Z F, CHEN Z H. Data mining andmathematics model [M]. Beijing: National Defense Industry Press, 2010 (in Chinese).
[7] ZHAO J L, ZHENG W. Study of fault diagnosismethod based on fuzzy Bayesian network and applica-tion in CTCS-3 train control system [C]//2013 IEEEInternational Conference on Intelligent Rail Trans-portation. [s.l.]: IEEE, 2013: 249-254.
[8] DONALD J B, DAMES A M, DEZON K F, et al.A case study of data quality in text mining clinicalprogress notes [J]. ACM Transaction on ManagementInformation System, 2015, 6(1): 1-21.
[9] WANG W J, LIU B X. Association rule-based network intrusion detection system [J]. Nuclear Electronics and Detection Technology, 2015, 35(2): 119-123 (inChinese).
[10] NADERPOUR M, LU J, ZHANG G Q. A fuzzy dynamic Bayesian network-based situation assessmentapproach [C]//2013 IEEE International Conference onFuzzy Systems. [s.l.]: IEEE, 2013: 1-8.
