Detecting Cybersecurity Attacks Using Different Network Features with LightGBM and XGBoost Learners. - Joffrey L. Leevy; John T. Hancock; Richard Zuech; Taghi M. Khoshgoftaar (CogMI, 2020)

Performance of CatBoost and XGBoost in Medicare Fraud Detection. - John T. Hancock; Taghi M. Khoshgoftaar (ICMLA, 2020)

Medicare Fraud Detection using CatBoost. - John T. Hancock; Taghi M. Khoshgoftaar (IRI, 2020)

Survey on categorical data for neural networks. - John T. Hancock; Taghi M. Khoshgoftaar (J. Big Data, 2020)

CatBoost for big data: an interdisciplinary review. - John T. Hancock; Taghi M. Khoshgoftaar (J. Big Data, 2020)

Leveraging LightGBM for Categorical Big Data. - John T. Hancock; Taghi M. Khoshgoftaar (BigDataService, 2021)

An Easy-to-Classify Approach for the Bot-IoT Dataset. - Joffrey L. Leevy; John T. Hancock; Taghi M. Khoshgoftaar; Jared M. Peterson (CogMI, 2021)

IoT Reconnaissance Attack Classification with Random Undersampling and Ensemble Feature Selection. - Joffrey L. Leevy; John T. Hancock; Taghi M. Khoshgoftaar; Naeem Seliya (CIC, 2021)

Detecting SQL Injection Web Attacks Using Ensemble Learners and Data Sampling. - Richard Zuech; John T. Hancock; Taghi M. Khoshgoftaar (CSR, 2021)

Detecting SSH and FTP Brute Force Attacks in Big Data. - John T. Hancock; Taghi M. Khoshgoftaar; Joffrey L. Leevy (ICMLA, 2021)

Detecting Information Theft Attacks in the Bot-IoT Dataset. - Joffrey L. Leevy; John T. Hancock; Taghi M. Khoshgoftaar; Jared M. Peterson (ICMLA, 2021)

Feature Popularity Between Different Web Attacks with Supervised Feature Selection Rankers. - Richard Zuech; John T. Hancock; Taghi M. Khoshgoftaar (ICMLA, 2021)

Impact of Hyperparameter Tuning in Classifying Highly Imbalanced Big Data. - John T. Hancock; Taghi M. Khoshgoftaar (IRI, 2021)

Detecting Web Attacks in Severely Imbalanced Network Traffic Data. - Richard Zuech; John T. Hancock; Taghi M. Khoshgoftaar (IRI, 2021)

Detecting cybersecurity attacks across different network features and learners. - Joffrey L. Leevy; John T. Hancock; Richard Zuech; Taghi M. Khoshgoftaar (J. Big Data, 2021)

Detecting web attacks using random undersampling and ensemble learners. - Richard Zuech; John T. Hancock; Taghi M. Khoshgoftaar (J. Big Data, 2021)

Gradient Boosted Decision Tree Algorithms for Medicare Fraud Detection. - John T. Hancock; Taghi M. Khoshgoftaar (SN Comput. Sci., 2021)

A Comparative Approach to Threshold Optimization for Classifying Imbalanced Data. - John T. Hancock; Justin M. Johnson; Taghi M. Khoshgoftaar (CIC, 2022)

Informative Evaluation Metrics for Highly Imbalanced Big Data Classification. - John T. Hancock; Taghi M. Khoshgoftaar; Justin M. Johnson (ICMLA, 2022)

Evaluating Performance Metrics for Credit Card Fraud Classification. - Joffrey L. Leevy; Taghi M. Khoshgoftaar; John T. Hancock (ICTAI, 2022)

Optimizing Ensemble Trees for Big Data Healthcare Fraud Detection. - John T. Hancock; Taghi M. Khoshgoftaar (IRI, 2022)

The Effects of Random Undersampling for Big Data Medicare Fraud Detection. - John T. Hancock; Taghi M. Khoshgoftaar; Justin M. Johnson (SOSE, 2022)

IoT information theft prediction using ensemble feature selection. - Joffrey L. Leevy; John T. Hancock; Taghi M. Khoshgoftaar; Jared M. Peterson (J. Big Data, 2022)

A new feature popularity framework for detecting cyberattacks using popular features. - Richard Zuech; John T. Hancock; Taghi M. Khoshgoftaar (J. Big Data, 2022)

Hyperparameter Tuning for Medicare Fraud Detection in Big Data. - John T. Hancock; Taghi M. Khoshgoftaar (SN Comput. Sci., 2022)

A Comparative Study of Model-Agnostic and Importance-Based Feature Selection Approaches. - Huanjing Wang; Qianxin Liang; John T. Hancock; Taghi M. Khoshgoftaar (CogMI, 2023)

Data Reduction to Improve the Performance of One-Class Classifiers on Highly Imbalanced Big Data. - John T. Hancock; Taghi M. Khoshgoftaar (ICMLA, 2023)

A Model-Agnostic Feature Selection Technique to Improve the Performance of One-Class Classifiers. - John T. Hancock; Richard A. Bauder; Taghi M. Khoshgoftaar (ICTAI, 2023)

One-Class Classifier Performance: Comparing Majority versus Minority Class Training. - Joffrey L. Leevy; John T. Hancock; Taghi M. Khoshgoftaar; Azadeh Abdollah Zadeh (ICTAI, 2023)

Assessing One-Class and Binary Classification Approaches for Identifying Medicare Fraud. - Joffrey L. Leevy; John T. Hancock; Taghi M. Khoshgoftaar (IRI, 2023)

Enhancing Credit Card Fraud Detection Through a Novel Ensemble Feature Selection Technique. - Huanjing Wang; Qianxin Liang; John T. Hancock; Taghi M. Khoshgoftaar (IRI, 2023)

Improving Medicare Fraud Detection through Big Data Size Reduction Techniques. - Huanjing Wang; John T. Hancock; Taghi M. Khoshgoftaar (SOSE, 2023)

Exploring Maximum Tree Depth and Random Undersampling in Ensemble Trees to Optimize the Classification of Imbalanced Big Data. - John T. Hancock; Taghi M. Khoshgoftaar (SN Comput. Sci., 2023)

Explainable machine learning models for Medicare fraud detection. - John T. Hancock; Richard A. Bauder; Huanjing Wang; Taghi M. Khoshgoftaar (J. Big Data, 2023)

Evaluating classifier performance with highly imbalanced Big Data. - John T. Hancock; Taghi M. Khoshgoftaar; Justin M. Johnson (J. Big Data, 2023)

Comparative analysis of binary and one-class classification techniques for credit card fraud data. - Joffrey L. Leevy; John T. Hancock; Taghi M. Khoshgoftaar (J. Big Data, 2023)

Investigating the effectiveness of one-class and binary classification for fraud detection. - Joffrey L. Leevy; John T. Hancock; Taghi M. Khoshgoftaar; Azadeh Abdollah Zadeh (J. Big Data, 2023)

Threshold optimization and random undersampling for imbalanced credit card data. - Joffrey L. Leevy; Justin M. Johnson; John T. Hancock; Taghi M. Khoshgoftaar (J. Big Data, 2023)

Data reduction techniques for highly imbalanced medicare Big Data. - John T. Hancock; Huanjing Wang; Taghi M. Khoshgoftaar; Qianxin Liang (J. Big Data, 2024)

Feature selection strategies: a comparative analysis of SHAP-value and importance-based methods. - Huanjing Wang; Qianxin Liang; John T. Hancock; Taghi M. Khoshgoftaar (J. Big Data, 2024)

A problem-agnostic approach to feature selection and analysis using SHAP. - John T. Hancock; Taghi M. Khoshgoftaar; Qianxin Liang (J. Big Data, 2025)