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Determination of Variables for a Bayesian Network and the Most Precious One

  • Esma Nur CiniciogluEmail author
  • Taylan Yenilmez
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 610)

Abstract

To ensure the quality of a learned Bayesian network out of limited data sets, evaluation and selection process of variables becomes necessary. With this purpose, two new variable selection criteria N 2 S j and N 3 S j are proposed in this research which show superior performance on limited data sets. These newly developed variable selection criteria with the existing ones from prior research are employed to create Bayesian networks from three different limited data sets. On each step of variable elimination, the performance of the resulting BNs are evaluated in terms of different network performance metrics. Furthermore, a new variable evaluation criteria, IH j , is proposed which measures the impact of a variable to all the other variables in the network. IH j serves as an indicator of the most important variables in the network which has a special importance for the use of BNs in social science research, where it is crucial to identify the most important factors in a setting.

Keywords

Bayesian networks Variable selection in Bayesian networks Importance hierarchy of variables in network Variable evaluation scores Limited data sets 

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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.Quantitative Methods Division, School of BusinessIstanbul UniversityIstanbulTurkey

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