Matches in SemOpenAlex for { <https://semopenalex.org/work/W128859095> ?p ?o ?g. }
- W128859095 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." @default.
- W128859095 created "2016-06-24" @default.
- W128859095 creator A5028680526 @default.
- W128859095 date "2007-01-01" @default.
- W128859095 modified "2023-10-08" @default.
- W128859095 title "Prediction Intervals for Class Probabilities" @default.
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- W128859095 hasPublicationYear "2007" @default.
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