Justin Johnson

The Effects of Random Undersampling for Big Data Medicare Fraud Detection

2022

We show it is possible to obtain better classification performance for experiments involving highly imbalanced Big Data with the application of data sampling techniques. We apply Random Undersampling to a publicly available Medicare insurance claims …

Visualizing and Understanding Recurrent Networks

2015 · arXiv (Cornell University)

Recurrent Neural Networks (RNNs), and specifically a variant with Long Short-Term Memory (LSTM), are enjoying renewed interest as a result of successful applications in a wide range of machine learning problems that involve sequential data. …