John T. Hancock

J. Big Data, December, 2024

Data reduction techniques for highly imbalanced medicare Big Data.

[BibT_eX]

[DOI]

J. Big Data, December, 2024

2023

Threshold optimization and random undersampling for imbalanced credit card data.

[BibT_eX]

[DOI]

J. Big Data, December, 2023

Investigating the effectiveness of one-class and binary classification for fraud detection.

[BibT_eX]

[DOI]

Azadeh Abdollah Zadeh

J. Big Data, December, 2023

Comparative analysis of binary and one-class classification techniques for credit card fraud data.

[BibT_eX]

[DOI]

J. Big Data, December, 2023

Evaluating classifier performance with highly imbalanced Big Data.

[BibT_eX]

[DOI]

J. Big Data, December, 2023

Explainable machine learning models for Medicare fraud detection.

[BibT_eX]

[DOI]

Richard A. Bauder

J. Big Data, December, 2023

Exploring Maximum Tree Depth and Random Undersampling in Ensemble Trees to Optimize the Classification of Imbalanced Big Data.

[BibT_eX]

[DOI]

SN Comput. Sci., September, 2023

Improving Medicare Fraud Detection through Big Data Size Reduction Techniques.

[BibT_eX]

[DOI]

Proceedings of the IEEE International Conference on Service-Oriented System Engineering, 2023

Enhancing Credit Card Fraud Detection Through a Novel Ensemble Feature Selection Technique.

[BibT_eX]

[DOI]

Proceedings of the 24th IEEE International Conference on Information Reuse and Integration for Data Science, 2023

Assessing One-Class and Binary Classification Approaches for Identifying Medicare Fraud.

[BibT_eX]

[DOI]

Proceedings of the 24th IEEE International Conference on Information Reuse and Integration for Data Science, 2023

One-Class Classifier Performance: Comparing Majority versus Minority Class Training.

[BibT_eX]

[DOI]

Azadeh Abdollah Zadeh

Proceedings of the 35th IEEE International Conference on Tools with Artificial Intelligence, 2023

A Model-Agnostic Feature Selection Technique to Improve the Performance of One-Class Classifiers.

[BibT_eX]

[DOI]

Richard A. Bauder

Proceedings of the 35th IEEE International Conference on Tools with Artificial Intelligence, 2023

Data Reduction to Improve the Performance of One-Class Classifiers on Highly Imbalanced Big Data.

[BibT_eX]

[DOI]

Proceedings of the International Conference on Machine Learning and Applications, 2023

A Comparative Study of Model-Agnostic and Importance-Based Feature Selection Approaches.

[BibT_eX]

[DOI]

Proceedings of the 5th IEEE International Conference on Cognitive Machine Intelligence, 2023

2022

Hyperparameter Tuning for Medicare Fraud Detection in Big Data.

[BibT_eX]

[DOI]

SN Comput. Sci., 2022

A new feature popularity framework for detecting cyberattacks using popular features.

[BibT_eX]

[DOI]

J. Big Data, 2022

IoT information theft prediction using ensemble feature selection.

[BibT_eX]

[DOI]

Jared M. Peterson

J. Big Data, 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

Optimizing Ensemble Trees for Big Data Healthcare Fraud Detection.

[BibT_eX]

[DOI]

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

Evaluating Performance Metrics for Credit Card Fraud Classification.

[BibT_eX]

[DOI]

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

Gradient Boosted Decision Tree Algorithms for Medicare Fraud Detection.

[BibT_eX]

[DOI]

SN Comput. Sci., 2021

Detecting web attacks using random undersampling and ensemble learners.

[BibT_eX]

[DOI]

J. Big Data, 2021

Detecting cybersecurity attacks across different network features and learners.

[BibT_eX]

[DOI]

J. Big Data, 2021

Detecting Web Attacks in Severely Imbalanced Network Traffic Data.

[BibT_eX]

[DOI]

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

Impact of Hyperparameter Tuning in Classifying Highly Imbalanced Big Data.

[BibT_eX]

[DOI]

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

Feature Popularity Between Different Web Attacks with Supervised Feature Selection Rankers.

[BibT_eX]

[DOI]

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

Detecting Information Theft Attacks in the Bot-IoT Dataset.

[BibT_eX]

[DOI]

Jared M. Peterson

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

Detecting SSH and FTP Brute Force Attacks in Big Data.

[BibT_eX]

[DOI]

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

Detecting SQL Injection Web Attacks Using Ensemble Learners and Data Sampling.

[BibT_eX]

[DOI]

Proceedings of the IEEE International Conference on Cyber Security and Resilience, 2021

IoT Reconnaissance Attack Classification with Random Undersampling and Ensemble Feature Selection.

[BibT_eX]

[DOI]

Naeem Seliya

Proceedings of the 7th IEEE International Conference on Collaboration and Internet Computing, 2021

An Easy-to-Classify Approach for the Bot-IoT Dataset.

[BibT_eX]

[DOI]

Jared M. Peterson

Proceedings of the Third IEEE International Conference on Cognitive Machine Intelligence, 2021

Leveraging LightGBM for Categorical Big Data.

[BibT_eX]

[DOI]

Proceedings of the Seventh IEEE International Conference on Big Data Computing Service and Applications, 2021

2020

CatBoost for big data: an interdisciplinary review.

[BibT_eX]

[DOI]

J. Big Data, 2020

Survey on categorical data for neural networks.

[BibT_eX]

[DOI]

J. Big Data, 2020

Medicare Fraud Detection using CatBoost.

[BibT_eX]

[DOI]

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

Performance of CatBoost and XGBoost in Medicare Fraud Detection.

[BibT_eX]

[DOI]

Proceedings of the 19th IEEE International Conference on Machine Learning and Applications, 2020

Detecting Cybersecurity Attacks Using Different Network Features with LightGBM and XGBoost Learners.

[BibT_eX]

[DOI]