Split Classification Model for Complex Clustered Data

Authors

• Katherine Gerot, University of Nebraska - LincolnFollow

Date of this Version

Spring 3-14-2022

Document Type

Thesis

Citation

Gerot, K. 2022. Split Classification Model for Complex Clustered Data. Undergraduate Honors Thesis. University of Nebraska-Lincoln.

Comments

Copyright Katherine Gerot 2022.

Abstract

Classification in high-dimensional data has generated tremendous interest in a multitude of fields. Data in higher dimensions often tend to reside in non-Euclidean metric space. This prevents Euclidean-based classification methodologies, such as regression, from reliably modeling the data. Many proposed models rely on computationally-complex embedding to convert the data to a more usable format. Others, namely the Support Vector Machine, rely on kernel manipulation to implicitly describe the "feature space" to arrive at a non-linear decision boundary. The proposed methodology in this paper seeks to classify complex data in a relatively computationally-simple and explainable manner.