J Shanghai Jiaotong Univ Sci

Journal of Shanghai Jiao Tong University (Science) ›› 2018, Vol. 23 ›› Issue (3): 423-.doi: 10.1007/s12204-018-1945-6

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

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

SHUAI Yong1;2 (帅勇), SONG Tailiang3 (宋太亮), WANG Jianping1 (王建平), ZHAN Wenbin4 (詹文斌)   

  1. (1. Technical Support Engineering Faculty, Armored Forced Engineering Academy, Beijing 100072, China; 2. Unit 68207 of PLA, Jiayuguan 735100, Gansu, China; 3. China Defense Science & Technology Information Center, Beijing 100142, China; 4. Communication Station, Air Force Equipment Research Academy, Beijing 100085, China)
  2. (1. Technical Support Engineering Faculty, Armored Forced Engineering Academy, Beijing 100072, China; 2. Unit 68207 of PLA, Jiayuguan 735100, Gansu, China; 3. China Defense Science & Technology Information Center, Beijing 100142, China; 4. Communication Station, Air Force Equipment Research Academy, Beijing 100085, China)
  • Online:2018-05-31 Published:2018-06-17
  • Contact: SHUAI Yong1;2 (帅勇) E-mail:alexshuai@sina.com

Abstract: 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.

Key words: reliability parameter| text mining| frequent pattern growth (FPG)| fuzzy Bayesian network (FBN)

摘要： 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.

关键词: reliability parameter| text mining| frequent pattern growth (FPG)| fuzzy Bayesian network (FBN)

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