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.
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Xu, M., Gan, D., Shen, J., An, B. (2018). Bayesian Network Retrieval Discrimination Criteria Model Based on Unbalanced Information. In: Chen, H., Fang, Q., Zeng, D., Wu, J. (eds) Smart Health. ICSH 2018. Lecture Notes in Computer Science(), vol 10983. Springer, Cham. https://doi.org/10.1007/978-3-030-03649-2_21
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DOI: https://doi.org/10.1007/978-3-030-03649-2_21
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