Date of Award
3-2024
Document Type
Thesis
Degree Name
Master of Science in Operations Research
Department
Department of Operational Sciences
First Advisor
Lance E. Champagne, PhD
Abstract
This research examined the class imbalance problem while training convolutional neural networks (CNN) by applying different techniques to combat this common issue. This research used a modified CIFAR-10 dataset along with a curated aerial image dataset. Methods covered included undersampling, oversampling, synthetic minority oversampling technique, Edited Nearest Neighbors and combinations of the aforementioned methods. This research found that undersampling methods tended to outperform oversampling methods. While undersampling methods showed a decrease in overall accuracy, the increase in minority class prediction performance was promising enough to warrant further investigation.
AFIT Designator
AFIT-ENS-MS-24-M-079
Recommended Citation
Foley, Colin W., "Techniques For Addressing Extreme Class Imbalance for Artificial Neural Networks Training" (2024). Theses and Dissertations. 7714.
https://scholar.afit.edu/etd/7714
Comments
A 12-month embargo was observed for posting this work on AFIT Scholar.
Distribution Statement A, Approved for Public Release. PA case number on file.