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Bayesian Network Retrieval Discrimination Criteria Model Based on Unbalanced Information

  • Man Xu
  • Dan Gan
  • Jiang ShenEmail author
  • Bang An
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10983)

Abstract

Unbalanced sample data are usually ignored in the process of case matching, but these data also lead to misclassification during case matching. To solve this problem, a discrimination criteria model based on the Bayesian network and corresponding algorithm is proposed in our paper. The Bayesian network cost sensitivity learning in this model uses the minimization theorem of loss function. We also introduce a ROC curve to evaluate the performance of the retrieval model and verify the validity of the model by using diagnostic data for clinical heart disease. Our results indicate that this method can effectively eliminate the cost sensitivity of imbalanced datasets and improve the accuracy of the retrieval results.

Keywords

Case matching Bayesian network Unbalanced data Cost-sensitivity 

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.Business SchoolNankai UniversityTianjinChina
  2. 2.College of Management and EconomicsTianjin UniversityTianjinChina

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