Justin M. Johnson

J. Big Data, December, 2023

Evaluating classifier performance with highly imbalanced Big Data.

[BibT_eX]

[DOI]

J. Big Data, December, 2023

Learning from Highly Imbalanced Big Data with Label Noise.

[BibT_eX]

[DOI]

Robert K. L. Kennedy

Int. J. Artif. Intell. Tools, August, 2023

Data-Centric AI for Healthcare Fraud Detection.

[BibT_eX]

[DOI]

SN Comput. Sci., July, 2023

2022

Encoding High-Dimensional Procedure Codes for Healthcare Fraud Detection.

[BibT_eX]

[DOI]

SN Comput. Sci., 2022

A Survey on Classifying Big Data with Label Noise.

[BibT_eX]

[DOI]

ACM J. Data Inf. Qual., 2022

The Effects of Random Undersampling for Big Data Medicare Fraud Detection.

[BibT_eX]

[DOI]

Proceedings of the IEEE International Conference on Service-Oriented System Engineering, 2022

Healthcare Provider Summary Data for Fraud Classification.

[BibT_eX]

[DOI]

Proceedings of the 23rd IEEE International Conference on Information Reuse and Integration for Data Science, 2022

GANs for Class-Imbalanced Data: A Meta-Analysis of GitHub Projects.

[BibT_eX]

[DOI]

Rick Sauber-Cole

Proceedings of the 34th IEEE International Conference on Tools with Artificial Intelligence, 2022

Cost-Sensitive Ensemble Learning for Highly Imbalanced Classification.

[BibT_eX]

[DOI]

Proceedings of the 21st IEEE International Conference on Machine Learning and Applications, 2022

Informative Evaluation Metrics for Highly Imbalanced Big Data Classification.

[BibT_eX]

[DOI]

Proceedings of the 21st IEEE International Conference on Machine Learning and Applications, 2022

A Comparative Approach to Threshold Optimization for Classifying Imbalanced Data.

[BibT_eX]

[DOI]

Proceedings of the 8th IEEE International Conference on Collaboration and Internet Computing, 2022

2021

Medical Provider Embeddings for Healthcare Fraud Detection.

[BibT_eX]

[DOI]

SN Comput. Sci., 2021

Encoding Techniques for High-Cardinality Features and Ensemble Learners.

[BibT_eX]

[DOI]

Proceedings of the 22nd IEEE International Conference on Information Reuse and Integration for Data Science, 2021

The Effects of Class Label Noise on Highly-Imbalanced Big Data.

[BibT_eX]

[DOI]

Robert K. L. Kennedy

Proceedings of the 33rd IEEE International Conference on Tools with Artificial Intelligence, 2021

Output Thresholding for Ensemble Learners and Imbalanced Big Data.

[BibT_eX]

[DOI]

Proceedings of the 33rd IEEE International Conference on Tools with Artificial Intelligence, 2021

Robust Thresholding Strategies for Highly Imbalanced and Noisy Data.

[BibT_eX]

[DOI]

Proceedings of the 20th IEEE International Conference on Machine Learning and Applications, 2021

2020

The Effects of Data Sampling with Deep Learning and Highly Imbalanced Big Data.

[BibT_eX]

[DOI]

Inf. Syst. Frontiers, 2020

Semantic Embeddings for Medical Providers and Fraud Detection.

[BibT_eX]

[DOI]

Proceedings of the 21st International Conference on Information Reuse and Integration for Data Science, 2020

Hcpcs2Vec: Healthcare Procedure Embeddings for Medicare Fraud Prediction.

[BibT_eX]

[DOI]

Proceedings of the 6th IEEE International Conference on Collaboration and Internet Computing, 2020

2019

Medicare fraud detection using neural networks.

[BibT_eX]

[DOI]

J. Big Data, 2019

Survey on deep learning with class imbalance.

[BibT_eX]

[DOI]

J. Big Data, 2019

Deep Learning and Data Sampling with Imbalanced Big Data.

[BibT_eX]

[DOI]

Proceedings of the 20th IEEE International Conference on Information Reuse and Integration for Data Science, 2019

Deep Learning and Thresholding with Class-Imbalanced Big Data.

[BibT_eX]

[DOI]