Journal of Shanghai Jiaotong University ›› 2013, Vol. 47 ›› Issue (08): 1246-1250.

• Aeronautics & Astronautics • Previous Articles     Next Articles

Estimation of Enumerative Missing Values Based on Relational Markov Model

CHEN Shuang1,2,3,SONG Jinyu1,DIAO Xingchun1,2,CAO Jianjun2
  

  1. (1. Institute of Command Information System, PLA University of Science and Technology, Nanjing 210007, China; 2. The 63rd Research Institute, PLA General Staff Headquarters, Nanjing 210007, China; 3. The 47th Division, Jilin Army Reservist Infantry, Jilin 132000, China)
  • Received:2012-10-22 Online:2013-08-29 Published:2013-08-29

Abstract:

Aimed at the data missing problem in data quality, a relational Markov model (RMM) based approach was proposed, which combined RMM and the dynamic attribute selection (DAS) method to estimate missing values, taking into  full account the relations between attributes and making maximum use of available information in complete cases to improve the estimation performance of missing values. This approach utilized the relational Markov model to compute the transition probabilities from source to target state, and fills in missing values using the maximum posterior probability (MaxPost) and probability proportional (ProProp) methods. Comparative experiments on well-known datasets verify the effectiveness and advantage of this approach.
 

Key words: data missing, relational Markov model(RMM), dynamic attribute selection(DAS), imputation method

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