Degree Name
MS (Master of Science)
Program
Mathematical Sciences
Date of Award
8-2010
Committee Chair or Co-Chairs
Jeff R. Knisley
Committee Members
Robert B. Gardner, Robert M. Price Jr.
Abstract
This thesis presents the area under the ROC (Receiver Operating Characteristics) curve, or abbreviated AUC, as an alternate measure for evaluating the predictive performance of ANNs (Artificial Neural Networks) classifiers. Conventionally, neural networks are trained to have total error converge to zero which may give rise to over-fitting problems. To ensure that they do not over fit the training data and then fail to generalize well in new data, it appears effective to stop training as early as possible once getting AUC sufficiently large via integrating ROC/AUC analysis into the training process. In order to reduce learning costs involving the imbalanced data set of the uneven class distribution, random sampling and k-means clustering are implemented to draw a smaller subset of representatives from the original training data set. Finally, the confidence interval for the AUC is estimated in a non-parametric approach.
Document Type
Thesis - unrestricted
Recommended Citation
Yu, Daoping, "Early Stopping of a Neural Network via the Receiver Operating Curve." (2010). Electronic Theses and Dissertations. Paper 1732. https://dc.etsu.edu/etd/1732
Copyright
Copyright by the authors.