The full text of this item is not available at this time because the student has placed this item under an embargo for a period of time. The Libraries are not authorized to provide a copy of this work during the embargo period, even for Texas A&M users with NetID.
Semiparametric Classification under a Forest Density Assumption
MetadataShow full item record
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.
Dorn, Mary Frances (2017). Semiparametric Classification under a Forest Density Assumption. Doctoral dissertation, Texas A & M University. Available electronically from