The use of generative adversarial networks to alleviate class imbalance in tabular data: a survey

The use of generative adversarial networks to alleviate class imbalance in tabular data: a survey

Abstract The existence of class imbalance in a dataset can greatly bias the classifier towards majority classification. This discrepancy can pose a serious problem for deep learning models, which require copious and diverse amounts of data to learn patterns and output classifications. Traditionally,...

Full description

Bibliographic Details
Main Authors:	Rick Sauber-Cole, Taghi M. Khoshgoftaar
Format:	Article
Language:	English
Published:	SpringerOpen 2022-08-01
Series:	Journal of Big Data
Subjects:	Class imbalance Generative adversarial networks Tabular data Deep learning
Online Access:	https://doi.org/10.1186/s40537-022-00648-6

Similar Items

On the Quality of Synthetic Generated Tabular Data
by: Erica Espinosa, et al.
Published: (2023-07-01)

Alleviating Class Imbalance in Actuarial Applications Using Generative Adversarial Networks
by: Kwanda Sydwell Ngwenduna, et al.
Published: (2021-03-01)

CasTGAN: Cascaded Generative Adversarial Network for Realistic Tabular Data Synthesis
by: Abdallah Alshantti, et al.
Published: (2024-01-01)

Survey on deep learning with class imbalance
by: Justin M. Johnson, et al.
Published: (2019-03-01)

Searching for Optimal Oversampling to Process Imbalanced Data: Generative Adversarial Networks and Synthetic Minority Over-Sampling Technique
by: Gayeong Eom, et al.
Published: (2023-08-01)

MCGAN: Modified Conditional Generative Adversarial Network (MCGAN) for Class Imbalance Problems in Network Intrusion Detection System
by: Kunda Suresh Babu, et al.
Published: (2023-02-01)

Predicting Ultra-High-Performance Concrete Compressive Strength Using Tabular Generative Adversarial Networks
by: Afshin Marani, et al.
Published: (2020-10-01)

Conditional Tabular Generative Adversarial Based Intrusion Detection System for Detecting Ddos and Dos Attacks on the Internet of Things Networks
by: Basim Ahmad Alabsi, et al.
Published: (2023-06-01)

CTCN: a novel credit card fraud detection method based on Conditional Tabular Generative Adversarial Networks and Temporal Convolutional Network
by: Xiaoyan Zhao, et al.
Published: (2023-10-01)

Deep Generative Models to Counter Class Imbalance: A Model-Metric Mapping With Proportion Calibration Methodology
by: Behroz Mirza, et al.
Published: (2021-01-01)

Low-shot learning and class imbalance: a survey
by: Preston Billion Polak, et al.
Published: (2024-01-01)

A survey on addressing high-class imbalance in big data
by: Joffrey L. Leevy, et al.
Published: (2018-11-01)

Impact of class distribution on the detection of slow HTTP DoS attacks using Big Data
by: Chad L. Calvert, et al.
Published: (2019-07-01)

A CNN-Based Novel Approach for Classification of Sacral Hiatus with GAN-Powered Tabular Data Set
by: Ferhat Kilic, et al.
Published: (2023-04-01)

A novel generative adversarial networks modelling for the class imbalance problem in high dimensional omics data
by: Samuel Cusworth, et al.
Published: (2024-03-01)

TabFairGAN: Fair Tabular Data Generation with Generative Adversarial Networks
by: Amirarsalan Rajabi, et al.
Published: (2022-05-01)

One-Class Classification Using Generative Adversarial Networks
by: Yang Yang, et al.
Published: (2019-01-01)

GAN-Based Tabular Data Generator for Constructing Synopsis in Approximate Query Processing: Challenges and Solutions
by: Mohammadali Fallahian, et al.
Published: (2024-01-01)

Generalization of Convolutional Neural Networks for ECG Classification Using Generative Adversarial Networks
by: Abdelrahman M. Shaker, et al.
Published: (2020-01-01)

R Basics with Tabular Data
by: Taryn Dewar
Published: (2016-12-01)

Sparse Hierarchical Table Ensemble–A Deep Learning Alternative for Tabular Data
by: Guy Farjon, et al.
Published: (2022-01-01)

Beyond Importance Scores: Interpreting Tabular ML by Visualizing Feature Semantics
by: Amirata Ghorbani, et al.
Published: (2021-12-01)

Adaptative Perturbation Patterns: Realistic Adversarial Learning for Robust Intrusion Detection
by: João Vitorino, et al.
Published: (2022-03-01)

Alleviating Class-Imbalance Data of Semiconductor Equipment Anomaly Detection Study
by: Da Hoon Seol, et al.
Published: (2023-01-01)

Lottery Ticket Structured Node Pruning for Tabular Datasets
by: Ryan Bluteau, et al.
Published: (2022-10-01)

Exploring generative adversarial networks and adversarial training
by: Afia Sajeeda, et al.
Published: (2022-06-01)

An Imbalanced Generative Adversarial Network-Based Approach for Network Intrusion Detection in an Imbalanced Dataset
by: Yamarthi Narasimha Rao, et al.
Published: (2023-01-01)

BWGAN-GP: An EEG Data Generation Method for Class Imbalance Problem in RSVP Tasks
by: Meng Xu, et al.
Published: (2022-01-01)

Enhancing Financial Fraud Detection through Addressing Class Imbalance Using Hybrid SMOTE-GAN Techniques
by: Patience Chew Yee Cheah, et al.
Published: (2023-09-01)

LF-Transformer: Latent Factorizer Transformer for Tabular Learning
by: Kwangtek Na, et al.
Published: (2024-01-01)

Learning From Imbalanced Data Using Triplet Adversarial Samples
by: Jaesub Yun, et al.
Published: (2023-01-01)

A Dynamic Weighted Tabular Method for Convolutional Neural Networks
by: Md. Ifraham Iqbal, et al.
Published: (2022-01-01)

Tabular Machine Learning Methods for Predicting Gas Turbine Emissions
by: Rebecca Potts, et al.
Published: (2023-08-01)

A Review of Tabular Data Synthesis Using GANs on an IDS Dataset
by: Stavroula Bourou, et al.
Published: (2021-09-01)

A Survey of Defect Detection Applications Based on Generative Adversarial Networks
by: Xiangjie He, et al.
Published: (2022-01-01)

Combining Hybrid Approach Redefinition-Multiclass Imbalance (HAR-MI) and Hybrid Sampling in Handling Multi-Class Imbalance and Overlapping
by: Hartono Hartono, et al.
Published: (2021-03-01)

A literature review on one-class classification and its potential applications in big data
by: Naeem Seliya, et al.
Published: (2021-09-01)

A Deep Learning-Based Pipeline for the Generation of Synthetic Tabular Data
by: Daniele Panfilo, et al.
Published: (2023-01-01)

Classification of Pediatric Posterior Fossa Tumors Using Convolutional Neural Network and Tabular Data
by: Moran Artzi, et al.
Published: (2021-01-01)

Identification of Multi-Class Drugs Based on Near Infrared Spectroscopy and Bidirectional Generative Adversarial Networks
by: Anbing Zheng, et al.
Published: (2021-02-01)