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Hybrid Reliability Parameter Selection Method Based on Text Mining, Frequent Pattern Growth Algorithm and Fuzzy Bayesian Network

  • Yong Shuai (帅勇)
  • Tailiang Song (宋太亮)
  • Jianping Wang (王建平)
  • Wenbin Zhan (詹文斌)
Article

Abstract

Reliability parameter selection is very important in the period of equipment project design and demonstration. 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 firstly, 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 fuzzifies the key attributes, which forms associated relationship in frequent item-sets and their main parameters, eliminates the subjective influence 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 effective.

Key words

reliability parameter text mining frequent pattern growth (FPG) fuzzy Bayesian network (FBN) 

CLC number

TP 311 

Document code

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Copyright information

© Shanghai Jiaotong University and Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  • Yong Shuai (帅勇)
    • 1
    • 2
  • Tailiang Song (宋太亮)
    • 3
  • Jianping Wang (王建平)
    • 1
  • Wenbin Zhan (詹文斌)
    • 4
  1. 1.Technical Support Engineering FacultyArmored Forced Engineering AcademyBeijingChina
  2. 2.Unit 68207 of PLAJiayuguanChina
  3. 3.China Defense Science & Technology Information CenterBeijingChina
  4. 4.Communication StationAir Force Equipment Research AcademyBeijingChina

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