A traditional machine learning environment is characterized by the training
and testing data being drawn from the same domain, therefore, having similar distribution
characteristics. In contrast, a transfer learning environment is characterized
by the training data having di erent distribution characteristics from the testing
data. Previous research on transfer learning has focused on the development and
evaluation of transfer learning algorithms using real-world datasets. Testing with
real-world datasets exposes an algorithm to a limited number of data distribution
di erences and does not exercise an algorithm's full capability and boundary limitations.
In this research, we de ne, implement, and deploy a transfer learning test
framework to test machine learning algorithms. The transfer learning test framework
is designed to create a wide-range of distribution di erences that are typically encountered
in a transfer learning environment. By testing with many di erent distribution
di erences, an algorithm's strong and weak points can be discovered and evaluated
against other algorithms.
This research additionally performs case studies that use the transfer learning
test framework. The rst case study focuses on measuring the impact of exposing algorithms to the Domain Class Imbalance distortion pro le. The next case study
uses the entire transfer learning test framework to evaluate both transfer learning
and traditional machine learning algorithms. The nal case study uses the transfer
learning test framework in conjunction with real-world datasets to measure the impact
of the base traditional learner on the performance of transfer learning algorithms.
Two additional experiments are performed that are focused on using unique realworld
datasets. The rst experiment uses transfer learning techniques to predict
fraudulent Medicare claims. The second experiment uses a heterogeneous transfer
learning method to predict phishing webgages. These case studies will be of interest to
researchers who develop and improve transfer learning algorithms. This research will
also be of bene t to machine learning practitioners in the selection of high-performing
transfer learning algorithms. / Includes bibliography. / Dissertation (Ph.D.)--Florida Atlantic University, 2017. / FAU Electronic Theses and Dissertations Collection
| Identifer | oai:union.ndltd.org:fau.edu/oai:fau.digital.flvc.org:fau_39809 |
| Contributors | Weiss, Karl Robert (author), Khoshgoftaar, Taghi M. (Thesis advisor), Florida Atlantic University (Degree grantor), College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science |
| Publisher | Florida Atlantic University |
| Source Sets | Florida Atlantic University |
| Language | English |
| Detected Language | English |
| Type | Electronic Thesis or Dissertation, Text |
| Format | 186 p., application/pdf |
| Rights | Copyright © is held by the author, with permission granted to Florida Atlantic University to digitize, archive and distribute this item for non-profit research and educational purposes. Any reuse of this item in excess of fair use or other copyright exemptions requires permission of the copyright holder., http://rightsstatements.org/vocab/InC/1.0/ |
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