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ORCIDTaghi M. Khoshgoftaar
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- affiliation: Florida Atlantic University, Boca Raton, Florida, USA
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2020 – today
- 2023
[j204]Justin M. Johnson
, Robert K. L. Kennedy, Taghi M. Khoshgoftaar:
Learning from Highly Imbalanced Big Data with Label Noise. Int. J. Artif. Intell. Tools 32(5): 2360003:1-2360003:20 (2023)- [j203]Zahra Salekshahrezaee, Joffrey L. Leevy, Taghi M. Khoshgoftaar:
The effect of feature extraction and data sampling on credit card fraud detection. J. Big Data 10(1): 6 (2023) - [j202]Clifford Kemp, Chad Calvert, Taghi M. Khoshgoftaar, Joffrey L. Leevy:
An approach to application-layer DoS detection. J. Big Data 10(1): 22 (2023) - [j201]John T. Hancock, Taghi M. Khoshgoftaar, Justin M. Johnson:
Evaluating classifier performance with highly imbalanced Big Data. J. Big Data 10(1): 42 (2023) - [j200]Joffrey L. Leevy, Justin M. Johnson, John T. Hancock, Taghi M. Khoshgoftaar:
Threshold optimization and random undersampling for imbalanced credit card data. J. Big Data 10(1): 58 (2023) - [j199]Safak Kayikci, Taghi M. Khoshgoftaar:
Breast cancer prediction using gated attentive multimodal deep learning. J. Big Data 10(1): 62 (2023) - [j198]Robert K. L. Kennedy, Zahra Salekshahrezaee, Flavio Villanustre, Taghi M. Khoshgoftaar:
Iterative cleaning and learning of big highly-imbalanced fraud data using unsupervised learning. J. Big Data 10(1): 106 (2023) - [j197]Joffrey L. Leevy, John T. Hancock, Taghi M. Khoshgoftaar:
Comparative analysis of binary and one-class classification techniques for credit card fraud data. J. Big Data 10(1): 118 (2023) - [j196]John T. Hancock, Richard A. Bauder, Huanjing Wang, Taghi M. Khoshgoftaar:
Explainable machine learning models for Medicare fraud detection. J. Big Data 10(1): 154 (2023) - [j195]Joffrey L. Leevy, John T. Hancock, Taghi M. Khoshgoftaar, Azadeh Abdollah Zadeh:
Investigating the effectiveness of one-class and binary classification for fraud detection. J. Big Data 10(1): 157 (2023) - [j194]Justin M. Johnson, Taghi M. Khoshgoftaar:
Data-Centric AI for Healthcare Fraud Detection. SN Comput. Sci. 4(4): 389 (2023) - [j193]John T. Hancock, Taghi M. Khoshgoftaar:
Exploring Maximum Tree Depth and Random Undersampling in Ensemble Trees to Optimize the Classification of Imbalanced Big Data. SN Comput. Sci. 4(5): 462 (2023) - [c396]Huanjing Wang, Qianxin Liang, John T. Hancock, Taghi M. Khoshgoftaar:
Enhancing Credit Card Fraud Detection Through a Novel Ensemble Feature Selection Technique. IRI 2023: 121-126 - [c395]Joffrey L. Leevy, John T. Hancock, Taghi M. Khoshgoftaar:
Assessing One-Class and Binary Classification Approaches for Identifying Medicare Fraud. IRI 2023: 267-272 - [c394]Robert K. L. Kennedy, Zahra Salekshahrezaee, Taghi M. Khoshgoftaar:
Unsupervised Anomaly Detection of Class Imbalanced Cognition Data Using an Iterative Cleaning Method. IRI 2023: 303-308 - [c393]Huanjing Wang, John T. Hancock, Taghi M. Khoshgoftaar:
Improving Medicare Fraud Detection through Big Data Size Reduction Techniques. SOSE 2023: 208-217 - 2022
- [j192]Joffrey L. Leevy, John T. Hancock, Taghi M. Khoshgoftaar, Jared M. Peterson:
IoT information theft prediction using ensemble feature selection. J. Big Data 9(1): 6 (2022) - [j191]Rick Sauber-Cole, Taghi M. Khoshgoftaar:
The use of generative adversarial networks to alleviate class imbalance in tabular data: a survey. J. Big Data 9(1): 98 (2022) - [j190]Richard Zuech, John T. Hancock, Taghi M. Khoshgoftaar:
A new feature popularity framework for detecting cyberattacks using popular features. J. Big Data 9(1): 119 (2022) - [j189]Justin M. Johnson, Taghi M. Khoshgoftaar:
A Survey on Classifying Big Data with Label Noise. ACM J. Data Inf. Qual. 14(4): 23:1-23:43 (2022) - [j188]Justin M. Johnson, Taghi M. Khoshgoftaar:
Encoding High-Dimensional Procedure Codes for Healthcare Fraud Detection. SN Comput. Sci. 3(5): 362 (2022) - [j187]John T. Hancock, Taghi M. Khoshgoftaar:
Hyperparameter Tuning for Medicare Fraud Detection in Big Data. SN Comput. Sci. 3(6): 440 (2022) - [c392]Robert K. L. Kennedy, Zahra Salekshahrezaee, Taghi M. Khoshgoftaar:
A Novel Approach for Unsupervised Learning of Highly-Imbalanced Data. CogMI 2022: 52-58 - [c391]John T. Hancock, Justin M. Johnson, Taghi M. Khoshgoftaar:
A Comparative Approach to Threshold Optimization for Classifying Imbalanced Data. CIC 2022: 135-142 - [c390]Erika Cardenas, Connor Shorten, Taghi M. Khoshgoftaar, Borivoje Furht:
A Comparison of House Price Classification with Structured and Unstructured Text Data. FLAIRS 2022 - [c389]Connor Shorten, Taghi M. Khoshgoftaar:
An Exploration of Consistency Learning with Data Augmentation. FLAIRS 2022 - [c388]Connor Shorten, Taghi M. Khoshgoftaar, Javad Hashemi, Safiya George Dalmida, David Newman, Debarshi Datta, Laurie Martinez, Candice Sareli, Paula Eckard:
Predicting the Severity of COVID-19 Respiratory Illness with Deep Learning. FLAIRS 2022 - [c387]John T. Hancock, Taghi M. Khoshgoftaar, Justin M. Johnson:
Informative Evaluation Metrics for Highly Imbalanced Big Data Classification. ICMLA 2022: 1419-1426 - [c386]Justin M. Johnson, Taghi M. Khoshgoftaar:
Cost-Sensitive Ensemble Learning for Highly Imbalanced Classification. ICMLA 2022: 1427-1434 - [c385]Joffrey L. Leevy, Taghi M. Khoshgoftaar, John T. Hancock:
Evaluating Performance Metrics for Credit Card Fraud Classification. ICTAI 2022: 1336-1341 - [c384]Rick Sauber-Cole, Taghi M. Khoshgoftaar, Justin M. Johnson:
GANs for Class-Imbalanced Data: A Meta-Analysis of GitHub Projects. ICTAI 2022: 1419-1424 - [c383]Connor Shorten, Erika Cardenas, Taghi M. Khoshgoftaar, Javad Hashemi, Safiya George Dalmida, David Newman, Debarshi Datta, Laurie Martinez, Candice Sareli, Paula Eckard:
Exploring Language-Interfaced Fine-Tuning for COVID-19 Patient Survival Classification. ICTAI 2022: 1449-1454 - [c382]Zahra Salekshahrezaee, Joffrey L. Leevy, Taghi M. Khoshgoftaar:
A Class-Imbalanced Study with Feature Extraction via PCA and Convolutional Autoencoder. IRI 2022: 63-68 - [c381]Justin M. Johnson, Taghi M. Khoshgoftaar:
Healthcare Provider Summary Data for Fraud Classification. IRI 2022: 236-242 - [c380]John T. Hancock, Taghi M. Khoshgoftaar:
Optimizing Ensemble Trees for Big Data Healthcare Fraud Detection. IRI 2022: 243-249 - [c379]John T. Hancock, Taghi M. Khoshgoftaar, Justin M. Johnson:
The Effects of Random Undersampling for Big Data Medicare Fraud Detection. SOSE 2022: 141-146 - 2021
- [j186]Joffrey L. Leevy, John T. Hancock, Richard Zuech, Taghi M. Khoshgoftaar:
Detecting cybersecurity attacks across different network features and learners. J. Big Data 8(1): 1-29 (2021) - [j185]Zahra Salekshahrezaee, Joffrey L. Leevy, Taghi M. Khoshgoftaar:
A reconstruction error-based framework for label noise detection. J. Big Data 8(1): 1-16 (2021) - [j184]Connor Shorten, Taghi M. Khoshgoftaar, Borko Furht:
Deep Learning applications for COVID-19. J. Big Data 8(1): 1-54 (2021) - [j183]Flavio Villanustre, Arjuna Chala, Roger Dev, Lili Xu, Jesse Shaw, Borko Furht, Taghi M. Khoshgoftaar:
Modeling and tracking Covid-19 cases using Big Data analytics on HPCC system platformm. J. Big Data 8(1): 33 (2021) - [j182]Richard Zuech, John T. Hancock, Taghi M. Khoshgoftaar:
Detecting web attacks using random undersampling and ensemble learners. J. Big Data 8(1): 75 (2021) - [j181]Connor Shorten, Taghi M. Khoshgoftaar, Borko Furht:
Text Data Augmentation for Deep Learning. J. Big Data 8(1): 101 (2021) - [j180]Naeem Seliya, Azadeh Abdollah Zadeh, Taghi M. Khoshgoftaar:
A literature review on one-class classification and its potential applications in big data. J. Big Data 8(1): 122 (2021) - [j179]John T. Hancock, Taghi M. Khoshgoftaar:
Gradient Boosted Decision Tree Algorithms for Medicare Fraud Detection. SN Comput. Sci. 2(4): 268 (2021) - [j178]Justin M. Johnson, Taghi M. Khoshgoftaar:
Medical Provider Embeddings for Healthcare Fraud Detection. SN Comput. Sci. 2(4): 276 (2021) - [c378]Joffrey L. Leevy, Taghi M. Khoshgoftaar, Jared M. Peterson:
Mitigating Class Imbalance for IoT Network Intrusion Detection: A Survey. BigDataService 2021: 143-148 - [c377]John T. Hancock, Taghi M. Khoshgoftaar:
Leveraging LightGBM for Categorical Big Data. BigDataService 2021: 149-154 - [c376]Robert K. L. Kennedy, Taghi M. Khoshgoftaar:
An Examination of Neural Networks on Cluster Computers. BigDataService 2021: 155-160 - [c375]Joffrey L. Leevy, John T. Hancock, Taghi M. Khoshgoftaar, Jared M. Peterson:
An Easy-to-Classify Approach for the Bot-IoT Dataset. CogMI 2021: 172-179 - [c374]Joffrey L. Leevy, John T. Hancock, Taghi M. Khoshgoftaar, Naeem Seliya:
IoT Reconnaissance Attack Classification with Random Undersampling and Ensemble Feature Selection. CIC 2021: 41-49 - [c373]Richard Zuech, John T. Hancock, Taghi M. Khoshgoftaar:
Detecting SQL Injection Web Attacks Using Ensemble Learners and Data Sampling. CSR 2021: 27-34 - [c372]Richard Zuech, John T. Hancock, Taghi M. Khoshgoftaar:
Feature Popularity Between Different Web Attacks with Supervised Feature Selection Rankers. ICMLA 2021: 30-37 - [c371]Connor Shorten, Taghi M. Khoshgoftaar:
KerasBERT: Modeling the Keras Language. ICMLA 2021: 219-226 - [c370]John T. Hancock, Taghi M. Khoshgoftaar, Joffrey L. Leevy:
Detecting SSH and FTP Brute Force Attacks in Big Data. ICMLA 2021: 760-765 - [c369]Joffrey L. Leevy, John T. Hancock, Taghi M. Khoshgoftaar, Jared M. Peterson:
Detecting Information Theft Attacks in the Bot-IoT Dataset. ICMLA 2021: 807-812 - [c368]Justin M. Johnson, Taghi M. Khoshgoftaar:
Robust Thresholding Strategies for Highly Imbalanced and Noisy Data. ICMLA 2021: 1182-1188 - [c367]Connor Shorten, Taghi M. Khoshgoftaar:
Investigating the Generalization of Image Classifiers with Augmented Test Sets. ICTAI 2021: 10-17 - [c366]Zahra Salekshahrezaee, Joffrey L. Leevy, Taghi M. Khoshgoftaar:
Feature Extraction for Class Imbalance Using a Convolutional Autoencoder and Data Sampling. ICTAI 2021: 217-223 - [c365]Robert K. L. Kennedy, Justin M. Johnson, Taghi M. Khoshgoftaar:
The Effects of Class Label Noise on Highly-Imbalanced Big Data. ICTAI 2021: 1427-1433 - [c364]Justin M. Johnson, Taghi M. Khoshgoftaar:
Output Thresholding for Ensemble Learners and Imbalanced Big Data. ICTAI 2021: 1449-1454 - [c363]Clifford Kemp, Chad Calvert, Taghi M. Khoshgoftaar:
Detecting Slow Application-Layer DoS Attacks With PCA. IRI 2021: 176-183 - [c362]Michael Crawford, Taghi M. Khoshgoftaar:
Using Inductive Transfer Learning to Improve Hotel Review Spam Detection. IRI 2021: 248-254 - [c361]Richard Zuech, John T. Hancock, Taghi M. Khoshgoftaar:
Detecting Web Attacks in Severely Imbalanced Network Traffic Data. IRI 2021: 267-273 - [c360]John T. Hancock, Taghi M. Khoshgoftaar:
Impact of Hyperparameter Tuning in Classifying Highly Imbalanced Big Data. IRI 2021: 348-354 - [c359]Justin M. Johnson, Taghi M. Khoshgoftaar:
Encoding Techniques for High-Cardinality Features and Ensemble Learners. IRI 2021: 355-361 - [c358]Aline Anacleto, Taghi M. Khoshgoftaar, Evangelos I. Kaisar:
Predicting Traffic Incidents in Road Networks Using Vehicle Detector Data. ITSC 2021: 1431-1436 - [c357]Jared M. Peterson, Joffrey L. Leevy, Taghi M. Khoshgoftaar:
A Review and Analysis of the Bot-IoT Dataset. SOSE 2021: 20-27 - 2020
- [j177]Richard A. Bauder, Taghi M. Khoshgoftaar:
A study on rare fraud predictions with big Medicare claims fraud data. Intell. Data Anal. 24(1): 141-161 (2020) - [j176]Justin M. Johnson, Taghi M. Khoshgoftaar:
The Effects of Data Sampling with Deep Learning and Highly Imbalanced Big Data. Inf. Syst. Frontiers 22(5): 1113-1131 (2020) - [j175]Tawfiq Hasanin, Taghi M. Khoshgoftaar, Joffrey L. Leevy, Richard A. Bauder:
Investigating class rarity in big data. J. Big Data 7(1): 23 (2020) - [j174]John T. Hancock, Taghi M. Khoshgoftaar:
Survey on categorical data for neural networks. J. Big Data 7(1): 28 (2020) - [j173]Joffrey L. Leevy, Taghi M. Khoshgoftaar, Richard A. Bauder, Naeem Seliya:
Investigating the relationship between time and predictive model maintenance. J. Big Data 7(1): 36 (2020) - [j172]Joffrey L. Leevy, Taghi M. Khoshgoftaar, Flavio Villanustre:
Survey on RNN and CRF models for de-identification of medical free text. J. Big Data 7(1): 73 (2020) - [j171]John T. Hancock, Taghi M. Khoshgoftaar:
CatBoost for big data: an interdisciplinary review. J. Big Data 7(1): 94 (2020) - [j170]Joffrey L. Leevy, Taghi M. Khoshgoftaar:
A survey and analysis of intrusion detection models based on CSE-CIC-IDS2018 Big Data. J. Big Data 7(1): 104 (2020) - [j169]Aaron N. Richter, Taghi M. Khoshgoftaar:
Sample size determination for biomedical big data with limited labels. Netw. Model. Anal. Health Informatics Bioinform. 9(1): 12 (2020) - [c356]Joffrey L. Leevy, John T. Hancock, Richard Zuech, Taghi M. Khoshgoftaar:
Detecting Cybersecurity Attacks Using Different Network Features with LightGBM and XGBoost Learners. CogMI 2020: 190-197 - [c355]Joffrey L. Leevy, Taghi M. Khoshgoftaar:
A Short Survey of LSTM Models for De-identification of Medical Free Text. CIC 2020: 117-124 - [c354]Justin M. Johnson, Taghi M. Khoshgoftaar:
Hcpcs2Vec: Healthcare Procedure Embeddings for Medicare Fraud Prediction. CIC 2020: 145-152 - [c353]Gabriel Castaneda, Paul Morris, Taghi M. Khoshgoftaar:
Evaluating The Number of Trainable Parameters on Deep Maxout and LReLU Networks for Visual Recognition. ICMLA 2020: 415-421 - [c352]John T. Hancock, Taghi M. Khoshgoftaar:
Performance of CatBoost and XGBoost in Medicare Fraud Detection. ICMLA 2020: 572-579 - [c351]Robert K. L. Kennedy, Taghi M. Khoshgoftaar:
Accelerated Deep Learning on HPCC Systems. ICMLA 2020: 847-852 - [c350]Clifford Kemp, Chad Calvert, Taghi M. Khoshgoftaar:
Detection Methods of Slow Read DoS Using Full Packet Capture Data. IRI 2020: 9-16 - [c349]John T. Hancock, Taghi M. Khoshgoftaar:
Medicare Fraud Detection using CatBoost. IRI 2020: 97-103 - [c348]Justin M. Johnson, Taghi M. Khoshgoftaar:
Semantic Embeddings for Medical Providers and Fraud Detection. IRI 2020: 224-230
2010 – 2019
- 2019
- [j168]Aaron N. Richter, Taghi M. Khoshgoftaar:
Efficient learning from big data for cancer risk modeling: A case study with melanoma. Comput. Biol. Medicine 110: 29-39 (2019) - [j167]Gabriel Castaneda, Paul Morris, Taghi M. Khoshgoftaar:
Maxout Networks for Visual Recognition. Int. J. Multim. Data Eng. Manag. 10(4): 1-25 (2019) - [j166]Joseph D. Prusa, Ryan Sagul, Taghi M. Khoshgoftaar:
Extracting Knowledge from Technical Reports for the Valuation of West Texas Intermediate Crude Oil Futures. Inf. Syst. Frontiers 21(1): 109-123 (2019) - [j165]Robert K. L. Kennedy, Taghi M. Khoshgoftaar, Flavio Villanustre, Timothy Humphrey:
A parallel and distributed stochastic gradient descent implementation using commodity clusters. J. Big Data 6: 16 (2019) - [j164]Matthew Herland, Richard A. Bauder, Taghi M. Khoshgoftaar:
The effects of class rarity on the evaluation of supervised healthcare fraud detection models. J. Big Data 6: 21 (2019) - [j163]Justin M. Johnson, Taghi M. Khoshgoftaar:
Survey on deep learning with class imbalance. J. Big Data 6: 27 (2019) - [j162]Connor Shorten, Taghi M. Khoshgoftaar:
A survey on Image Data Augmentation for Deep Learning. J. Big Data 6: 60 (2019) - [j161]Justin M. Johnson, Taghi M. Khoshgoftaar:
Medicare fraud detection using neural networks. J. Big Data 6: 63 (2019) - [j160]Chad Calvert, Taghi M. Khoshgoftaar:
Impact of class distribution on the detection of slow HTTP DoS attacks using Big Data. J. Big Data 6: 67 (2019) - [j159]Victor M. Herrera, Taghi M. Khoshgoftaar, Flavio Villanustre, Borko Furht:
Random forest implementation and optimization for Big Data analytics on LexisNexis's high performance computing cluster platform. J. Big Data 6: 68 (2019) - [j158]Tawfiq Hasanin, Taghi M. Khoshgoftaar, Joffrey L. Leevy, Naeem Seliya:
Examining characteristics of predictive models with imbalanced big data. J. Big Data 6: 69 (2019) - [j157]Gabriel Castaneda, Paul Morris, Taghi M. Khoshgoftaar:
Evaluation of maxout activations in deep learning across several big data domains. J. Big Data 6: 72 (2019) - [j156]Tawfiq Hasanin, Taghi M. Khoshgoftaar, Joffrey L. Leevy, Richard A. Bauder:
Severely imbalanced Big Data challenges: investigating data sampling approaches. J. Big Data 6: 107 (2019) - [j155]Aaron N. Richter, Taghi M. Khoshgoftaar:
Melanoma risk modeling from limited positive samples. Netw. Model. Anal. Health Informatics Bioinform. 8(1): 7 (2019) - [c347]Tawfiq Hasanin, Taghi M. Khoshgoftaar, Joffrey L. Leevy, Naeem Seliya:
Investigating Random Undersampling and Feature Selection on Bioinformatics Big Data. BigDataService 2019: 346-356 - [c346]Gabriel Castaneda, Paul Morris, Taghi M. Khoshgoftaar:
Maxout Neural Network for Big Data Medical Fraud Detection. BigDataService 2019: 357-362 - [c345]Stevens Dormezil, Taghi M. Khoshgoftaar, Federica Robinson-Bryant:
Differentiating between Educational Data Mining and Learning Analytics: A Bibliometric Approach. EDM (Workshops) 2019: 17-22 - [c344]Gabriel Castaneda, Paul Morris, Joseph D. Prusa, Taghi M. Khoshgoftaar:
Investigation of Maxout Activations on Convolutional Neural Networks for Big Data Text Sentiment Analysis. FLAIRS 2019: 250-256 - [c343]Chad Calvert, Clifford Kemp, Taghi M. Khoshgoftaar, Maryam M. Najafabadi:
Detecting Slow HTTP POST DoS Attacks Using Netflow Features. FLAIRS 2019: 387-390 - [c342]Justin M. Johnson, Taghi M. Khoshgoftaar:
Deep Learning and Thresholding with Class-Imbalanced Big Data. ICMLA 2019: 755-762 - [c341]Aaron N. Richter, Taghi M. Khoshgoftaar:
Learning Curve Estimation with Large Imbalanced Datasets. ICMLA 2019: 763-768 - [c340]Huanjing Wang, Taghi M. Khoshgoftaar:
A Study on Software Metric Selection for Software Fault Prediction. ICMLA 2019: 1045-1050 - [c339]Joffrey L. Leevy, Taghi M. Khoshgoftaar, Richard A. Bauder, Naeem Seliya:
The Effect of Time on the Maintenance of a Predictive Model. ICMLA 2019: 1891-1896 - [c338]Aaron N. Richter, Taghi M. Khoshgoftaar:
Approximating Learning Curves for Imbalanced Big Data with Limited Labels. ICTAI 2019: 237-242 - [c337]Chad Calvert, Taghi M. Khoshgoftaar:
Threshold Based Optimization of Performance Metrics with Severely Imbalanced Big Security Data. ICTAI 2019: 1328-1334 - [c336]Richard A. Bauder, Matthew Herland, Taghi M. Khoshgoftaar:
Evaluating Model Predictive Performance: A Medicare Fraud Detection Case Study. IRI 2019: 9-14 - [c335]Tawfiq Hasanin, Taghi M. Khoshgoftaar, Joffrey L. Leevy:
A Comparison of Performance Metrics with Severely Imbalanced Network Security Big Data. IRI 2019: 83-88 - [c334]Gabriel Castaneda, Paul Morris, Taghi M. Khoshgoftaar:
Deep Learning with Maxout Activations for Visual Recognition and Verification. IRI 2019: 135-142 - [c333]Justin M. Johnson, Taghi M. Khoshgoftaar:
Deep Learning and Data Sampling with Imbalanced Big Data. IRI 2019: 175-183 - [e4]M. Arif Wani, Taghi M. Khoshgoftaar, Dingding Wang, Huanjing Wang, Naeem Seliya:
18th IEEE International Conference On Machine Learning And Applications, ICMLA 2019, Boca Raton, FL, USA, December 16-19, 2019. IEEE 2019, ISBN 978-1-7281-4550-1 [contents] - 2018
- [j154]Aaron N. Richter, Taghi M. Khoshgoftaar:
A review of statistical and machine learning methods for modeling cancer risk using structured clinical data. Artif. Intell. Medicine 90: 1-14 (2018) - [j153]