Semiparametric Classification under a Forest Density Assumption
| dc.contributor.advisor | Spiegelman, Cliff | |
| dc.contributor.committeeMember | Bryant, Vaughn | |
| dc.contributor.committeeMember | Mallick, Bani | |
| dc.contributor.committeeMember | Johnson, Valen | |
| dc.creator | Dorn, Mary Frances | |
| dc.date.accessioned | 2017-08-21T14:35:32Z | |
| dc.date.available | 2019-05-01T06:08:28Z | |
| dc.date.created | 2017-05 | |
| dc.date.issued | 2017-04-24 | |
| dc.date.submitted | May 2017 | |
| dc.date.updated | 2017-08-21T14:35:32Z | |
| dc.description.abstract | This dissertation proposes a new semiparametric approach for binary classification that exploits the modeling flexibility of sparse graphical models. This approach is based on non-parametrically estimated densities, which are notoriously difficult to obtain when the number of dimensions is even moderately large. In this work, it is assumed that each class can be well-represented by a family of undirected sparse graphical models, specifically a forest-structured distribution. By making this assumption, non-parametric estimation of only one- and two-dimensional marginal densities are required to transform the data into a space where a linear classifier is optimal. This work proves convergence results for the forest density classifier under certain conditions. Its performance is illustrated by comparing it to several state-of-the-art classifiers on simulated forest-distributed data as well as a panel of real datasets from different domains. These experiments indicate that the proposed method is competitive with popular methods across a wide range of applications. | en |
| dc.format.mimetype | application/pdf | |
| dc.identifier.uri | https://hdl.handle.net/1969.1/161361 | |
| dc.language.iso | en | |
| dc.subject | classification | en |
| dc.subject | nonparametric density estimation | en |
| dc.subject | forests | en |
| dc.subject | machine learning | en |
| dc.title | Semiparametric Classification under a Forest Density Assumption | en |
| dc.type | Thesis | en |
| dc.type.material | text | en |
| local.embargo.terms | 2019-05-01 | |
| local.etdauthor.orcid | 0000-0001-9420-0844 | |
| thesis.degree.department | Statistics | en |
| thesis.degree.discipline | Statistics | en |
| thesis.degree.grantor | Texas A & M University | en |
| thesis.degree.level | Doctoral | en |
| thesis.degree.name | Doctor of Philosophy | en |