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    A Bayesian Modelling Approach with Balancing Informative Prior for Analysing Imbalanced Data

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    Author
    Klein, K; Hennig, S; Paul, SK
    Date
    2016-04-12
    Source Title
    PLoS One
    Publisher
    PUBLIC LIBRARY SCIENCE
    University of Melbourne Author/s
    Paul, Sanjoy
    Affiliation
    Medicine and Radiology
    Metadata
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    Document Type
    Journal Article
    Citations
    Klein, K., Hennig, S. & Paul, S. K. (2016). A Bayesian Modelling Approach with Balancing Informative Prior for Analysing Imbalanced Data. PLOS ONE, 11 (4), https://doi.org/10.1371/journal.pone.0152700.
    Access Status
    Open Access
    URI
    http://hdl.handle.net/11343/257901
    DOI
    10.1371/journal.pone.0152700
    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.

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