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      Prediction Intervals for Class Probabilities

      Yu, Xiaofeng
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      Yu, X. (2007). Prediction Intervals for Class Probabilities (Thesis, Master of Science (MSc)). The University of Waikato, Hamilton, New Zealand. Retrieved from https://hdl.handle.net/10289/2436
      Permanent Research Commons link: https://hdl.handle.net/10289/2436
      Abstract
      Prediction intervals for class probabilities are of interest in machine learning because they

      can quantify the uncertainty about the class probability estimate for a test instance. The

      idea is that all likely class probability values of the test instance are included, with a

      pre-specified confidence level, in the calculated prediction interval. This thesis proposes a

      probabilistic model for calculating such prediction intervals. Given the unobservability of

      class probabilities, a Bayesian approach is employed to derive a complete distribution of the

      class probability of a test instance based on a set of class observations of training instances

      in the neighbourhood of the test instance. A random decision tree ensemble learning

      algorithm is also proposed, whose prediction output constitutes the neighbourhood that

      is used by the Bayesian model to produce a PI for the test instance. The Bayesian

      model, which is used in conjunction with the ensemble learning algorithm and the standard

      nearest-neighbour classifier, is evaluated on artificial datasets and modified real datasets.
      Date
      2007
      Type
      Thesis
      Degree Name
      Master of Science (MSc)
      Publisher
      The University of Waikato
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      All items in Research Commons are provided for private study and research purposes and are protected by copyright with all rights reserved unless otherwise indicated.
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      • Masters Degree Theses [2385]
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