| 2022 | ICMLA | Informative Evaluation Metrics for Highly Imbalanced Big Data Classification. | John T. Hancock, Taghi M. Khoshgoftaar, Justin M. Johnson |
| 2022 | ICMLA | Cost-Sensitive Ensemble Learning for Highly Imbalanced Classification. | Justin M. Johnson, Taghi M. Khoshgoftaar |
| 2022 | IRI | Healthcare Provider Summary Data for Fraud Classification. | Justin M. Johnson, Taghi M. Khoshgoftaar |
| 2022 | ICTAI | GANs for Class-Imbalanced Data: A Meta-Analysis of GitHub Projects. | Rick Sauber-Cole, Taghi M. Khoshgoftaar, Justin M. Johnson |
| 2021 | ICMLA | Robust Thresholding Strategies for Highly Imbalanced and Noisy Data. | Justin M. Johnson, Taghi M. Khoshgoftaar |
| 2021 | IRI | Encoding Techniques for High-Cardinality Features and Ensemble Learners. | Justin M. Johnson, Taghi M. Khoshgoftaar |
| 2021 | ICTAI | Output Thresholding for Ensemble Learners and Imbalanced Big Data. | Justin M. Johnson, Taghi M. Khoshgoftaar |
| 2021 | ICTAI | The Effects of Class Label Noise on Highly-Imbalanced Big Data. | Robert K. L. Kennedy, Justin M. Johnson, Taghi M. Khoshgoftaar |
| 2020 | IRI | Semantic Embeddings for Medical Providers and Fraud Detection. | Justin M. Johnson, Taghi M. Khoshgoftaar |
| 2019 | ICMLA | Deep Learning and Thresholding with Class-Imbalanced Big Data. | Justin M. Johnson, Taghi M. Khoshgoftaar |
| 2019 | IRI | Deep Learning and Data Sampling with Imbalanced Big Data. | Justin M. Johnson, Taghi M. Khoshgoftaar |