J
Justin M. Johnson
Researcher at Florida Atlantic University
Publications - 8
Citations - 1571
Justin M. Johnson is an academic researcher from Florida Atlantic University. The author has contributed to research in topics: Artificial neural network & Deep learning. The author has an hindex of 6, co-authored 8 publications receiving 634 citations.
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Journal ArticleDOI
Survey on deep learning with class imbalance
TL;DR: Examination of existing deep learning techniques for addressing class imbalanced data finds that research in this area is very limited, that most existing work focuses on computer vision tasks with convolutional neural networks, and that the effects of big data are rarely considered.
Journal ArticleDOI
Medicare fraud detection using neural networks
TL;DR: This is the first study to compare multiple data-level and algorithm-level deep learning methods across a range of class distributions and a unique analysis of the relationship between minority class size and optimal decision threshold and state-of-the-art performance on the given Medicare fraud detection task.
Journal ArticleDOI
The Effects of Data Sampling with Deep Learning and Highly Imbalanced Big Data
TL;DR: This work uses three data sets of varying complexity to evaluate data sampling strategies for treating high class imbalance with deep neural networks and big data and suggests that each data set is uniquely sensitive to imbalance and sample size.
Proceedings ArticleDOI
Deep Learning and Data Sampling with Imbalanced Big Data
TL;DR: This study evaluates the use of deep learning and data sampling on a class-imbalanced Big Data problem, i.e. Medicare fraud detection, and is the first study to provide statistical results comparing ROS, RUS, and ROS-RUS deep learning methods across a range of class distributions.
Book ChapterDOI
Thresholding Strategies for Deep Learning with Highly Imbalanced Big Data
TL;DR: In this article, the authors compare the performance of the default threshold of 0.5 with the prior probability of the positive class prior in a wide range of class imbalance levels using three real-world data sets, and compare default threshold results to two alternative thresholding strategies.