A Bayesian modelling approach with balancing informative prior for analysing imbalanced data

Klein, Kerenaftali, Hennig, Stefanie and Paul, Sanjoy Ketan (2016) A Bayesian modelling approach with balancing informative prior for analysing imbalanced data. PLoS One, 11 4: e0152700.1-e0152700.12. doi:10.1371/journal.pone.0152700


 
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Author Klein, Kerenaftali
Hennig, Stefanie
Paul, Sanjoy Ketan
Title A Bayesian modelling approach with balancing informative prior for analysing imbalanced data
Journal name PLoS One   Check publisher's open access policy
ISSN 1932-6203
Publication date 2016
Sub-type Article (original research)
DOI 10.1371/journal.pone.0152700
Open Access Status DOI
Volume 11
Issue 4
Start page e0152700.1
End page e0152700.12
Total pages 12
Place of publication San Francisco, CA, United States
Publisher Public Library of Science
Collection year 2017
Language eng
Abstract When a dataset is imbalanced, the prediction of the scarcely-sampled subpopulation can be over-influenced by the population contributing to the majority of the data. The aim of this study was to develop a Bayesian modelling approach with balancing informative prior so that the influence of imbalance to the overall prediction could be minimised. The new approach was developed in order to weigh the data in favour of the smaller subset(s). The method was assessed in terms of bias and precision in predicting model parameter estimates of simulated datasets. Moreover, the method was evaluated in predicting optimal dose levels of tobramycin for various age groups in a motivating example. The bias estimates using the balancing informative prior approach were smaller than those generated using the conventional approach which was without the consideration for the imbalance in the datasets. The precision estimates were also superior. The method was further evaluated in a motivating example of optimal dosage prediction of tobramycin. The resulting predictions also agreed well with what had been reported in the literature. The proposed Bayesian balancing informative prior approach has shown a real potential to adequately weigh the data in favour of smaller subset(s) of data to generate robust prediction models
Q-Index Code C1
Q-Index Status Provisional Code
Institutional Status UQ

Document type: Journal Article
Sub-type: Article (original research)
Collections: HERDC Pre-Audit
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Created: Wed, 13 Apr 2016, 08:48:29 EST by Dr Stefanie Hennig on behalf of School of Pharmacy