Date of Award
2019
Document Type
Thesis
Publication Status
Version of Record
Submission Date
October 2026
Thesis/Dissertation Advisor [Chair]
Khoshgoftaar, Taghi M.
Abstract
Effective classification with imbalanced data is an important area of research, as high class imbalance is naturally inherent in many real-world applications, e.g. anomaly detection. Modeling such skewed data distributions is often very difficult, and non-standard methods are sometimes required to combat these negative effects. These challenges have been studied thoroughly using traditional machine learning algorithms, but very little empirical work exists in the area of deep learning with class imbalanced big data. Following an in-depth survey of deep learning methods for addressing class imbalance, we evaluate various methods for addressing imbalance on the task of detecting Medicare fraud, a big data problem characterized by extreme class imbalance. Case studies herein demonstrate the impact of class imbalance on neural networks, evaluate the efficacy of data-level and algorithm-level methods, and achieve state-of-the-art results on the given Medicare data set. Results indicate that combining under-sampling and over-sampling maximizes both performance and efficiency.
Extent
129 p.
Recommended Citation
Johnson, Justin Matthew, "An Evaluation of Deep Learning with Class Imbalanced Big Data" (2019). Electronic Theses and Dissertations 2010 - 2019. 2092.
https://digitalcommons.fau.edu/etd_general_6/2092
Identifier
FA00013221
Comments
Includes bibliography.|Thesis (M.S.)--Florida Atlantic University, 2019.|FAU Electronic Theses and Dissertations Collection|Includes bibliography.