dc.contributor.author | Butler, Michael | |
dc.date.accessioned | 2014-08-22T13:49:48Z | |
dc.date.available | 2014-08-22T13:49:48Z | |
dc.date.issued | 2014-08-22 | |
dc.identifier.uri | http://hdl.handle.net/10222/53996 | |
dc.description.abstract | When a clinician diagnoses a patient, they do so by choosing one from many possible diagnoses. This is a laborious process, one that requires input from many different sources of information. It would be useful to have an objective tool to give a prediction of a patient’s diagnosis using readily available clinical information.\\
Although this would be useful, one needs to still choose from many different possible choices, a large scale multi-class problem that conventional classification methods may not be suited to solve. We describe a method that assigns a class label to an observation from a large number of class possible labels, and gives the probability of said observation having such. The method uses a combination of support vector machines, and an agglomerative hierarchical clustering algorithm to perform the task. We display the performance of the method on a benchmark problem, and a hospital-based dataset from Halifax, NS. | en_US |
dc.language.iso | en | en_US |
dc.subject | Machine-learning | en_US |
dc.subject | Data mining | en_US |
dc.subject | Multi-class | en_US |
dc.title | A HIERARCHICAL STRUCTURED MACHINE-LEARNING METHOD FOR LARGE-SCALE MULTI-CLASS PROBLEMS | en_US |
dc.type | Thesis | en_US |
dc.date.defence | 2014-08-13 | |
dc.contributor.department | Department of Mathematics & Statistics - Statistics Division | en_US |
dc.contributor.degree | Master of Science | en_US |
dc.contributor.external-examiner | n/a | en_US |
dc.contributor.graduate-coordinator | Hong Gu | en_US |
dc.contributor.thesis-reader | Kenney, Tobey | en_US |
dc.contributor.thesis-reader | Smith, Bruce | en_US |
dc.contributor.thesis-supervisor | Hong Gu | en_US |
dc.contributor.ethics-approval | Not Applicable | en_US |
dc.contributor.manuscripts | Not Applicable | en_US |
dc.contributor.copyright-release | Not Applicable | en_US |