Prediction Intervals for Class Probabilities
Yu, X. (2007). Prediction Intervals for Class Probabilities (Thesis, Master of Science (MSc)). The University of Waikato, Hamilton, New Zealand. Retrieved from http://hdl.handle.net/10289/2436
Permanent Research Commons link: http://hdl.handle.net/10289/2436
Prediction intervals for class probabilities are of interest in machine learning because theycan quantify the uncertainty about the class probability estimate for a test instance. Theidea is that all likely class probability values of the test instance are included, with apre-specified confidence level, in the calculated prediction interval. This thesis proposes aprobabilistic model for calculating such prediction intervals. Given the unobservability ofclass probabilities, a Bayesian approach is employed to derive a complete distribution of theclass probability of a test instance based on a set of class observations of training instancesin the neighbourhood of the test instance. A random decision tree ensemble learningalgorithm is also proposed, whose prediction output constitutes the neighbourhood thatis used by the Bayesian model to produce a PI for the test instance. The Bayesianmodel, which is used in conjunction with the ensemble learning algorithm and the standardnearest-neighbour classifier, is evaluated on artificial datasets and modified real datasets.
The University of Waikato
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