Prediction Intervals for Class Probabilities

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dc.contributor.authorYu, Xiaofengen_NZ
dc.date.accessioned2007-02-19T15:12:34Z
dc.date.available2007-08-21T16:37:08Z
dc.date.issued2007en_NZ
dc.description.abstractPrediction 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.en_NZ
dc.format.mimetypeapplication/pdf
dc.identifier.citationYu, 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/10289/2436en
dc.identifier.urihttps://hdl.handle.net/10289/2436
dc.language.isoen
dc.publisherThe University of Waikatoen_NZ
dc.rightsAll items in Research Commons are provided for private study and research purposes and are protected by copyright with all rights reserved unless otherwise indicated.
dc.subjectbayesian classifieren_NZ
dc.subjectmachine learningen_NZ
dc.titlePrediction Intervals for Class Probabilitiesen_NZ
dc.typeThesisen_NZ
pubs.place-of-publicationHamilton, New Zealanden_NZ
thesis.degree.disciplineSchool of Computing and Mathematical Sciencesen_NZ
thesis.degree.grantorUniversity of Waikatoen_NZ
thesis.degree.levelMasters
thesis.degree.nameMaster of Science (MSc)en_NZ
uow.date.accession2007-02-19T15:12:34Zen_NZ
uow.date.available2007-08-21T16:37:08Zen_NZ
uow.date.migrated2009-06-09T23:29:14Zen_NZ
uow.identifier.adthttp://adt.waikato.ac.nz/public/adt-uow20070219.151234en_NZ
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