Imbalanced Data

Imbalanced data, where one class significantly outnumbers others, poses a major challenge for machine learning models, hindering their ability to accurately predict minority classes. Current research focuses on mitigating this imbalance through techniques like data augmentation (e.g., using generative adversarial networks or variational autoencoders), cost-sensitive learning, and novel loss functions designed to be robust to skewed distributions; various model architectures, including Recurrent Neural Networks, Transformers, and ensemble methods, are being adapted and evaluated for their effectiveness in these scenarios. Addressing imbalanced data is crucial for improving the reliability and fairness of machine learning models across diverse applications, from medical image analysis and fraud detection to cybersecurity and industrial process monitoring.

Papers

October 23, 2023

Invariant Feature Regularization for Fair Face Recognition
Jiali Ma, Zhongqi Yue, Kagaya Tomoyuki, Suzuki Tomoki, Karlekar Jayashree, Sugiri Pranata, Hanwang Zhang
Face Recognition Imbalanced Data Conditional Invariant

October 11, 2023

September 29, 2023

An Investigation Into Race Bias in Random Forest Models Based on Breast DCE-MRI Derived Radiomics Features
Mohamed Huti, Tiarna Lee, Elinor Sawyer, Andrew P. King
Random Forest Radiomics Feature Imbalanced Data Racial Bias Random Forest Model Dce Mri

September 18, 2023

Neural Collapse for Unconstrained Feature Model under Cross-entropy Loss with Imbalanced Data
Wanli Hong, Shuyang Ling
Class Imbalance Cross Entropy Loss Neural Collapse Imbalanced Data Unconstrained Feature Model Class Relevant

August 29, 2023

From SMOTE to Mixup for Deep Imbalanced Classification
Wei-Chao Cheng, Tan-Ha Mai, Hsuan-Tien Lin
Imbalanced Data Mixup Method Spatio Temporal Mixup Mechanism Imbalanced Learning Synthetic Minority Oversampling Technique Deep Imbalanced

August 28, 2023

Distributed Dual Coordinate Ascent with Imbalanced Data on a General Tree Network
Myung Cho, Lifeng Lai, Weiyu Xu
Machine Learning Empirical Risk Minimization Imbalanced Data Structured Graph

June 30, 2023

The Effect of Balancing Methods on Model Behavior in Imbalanced Classification Problems
Adrian Stando, Mustafa Cavus, Przemysław Biecek
Machine Learning Model Mixed Effect Model Bias Imbalanced Data Balancing Strategy Imbalanced Classification Model Comparison Model Behavior Balanced Distribution

May 11, 2023

Entropy-Aware Similarity for Balanced Clustering: A Case Study with Melanoma Detection
Seok Bin Son, Soohyun Park, Joongheon Kim
Case Study Data Clustering Imbalanced Data Melanoma Detection Feature Similarity

April 12, 2023

Towards Understanding How Data Augmentation Works with Imbalanced Data
Damien A. Dablain, Nitesh V. Chawla
Data Augmentation Machine Learning Model Imbalanced Data Regularization Effect Balanced Data

March 27, 2023

Evaluating XGBoost for Balanced and Imbalanced Data: Application to Fraud Detection
Gissel Velarde, Anindya Sudhir, Sanjay Deshmane, Anuj Deshmunkh, Khushboo Sharma, Vaibhav Joshi
Application Proficiency Fraud Detection Imbalanced Data Data Imbalance Imbalanced Datasets XGBoost Model Balanced Approach

March 11, 2023

Credit Card Fraud Detection Using Enhanced Random Forest Classifier for Imbalanced Data
AlsharifHasan Mohamad Aburbeian, Huthaifa I. Ashqar
Random Forest Fraud Detection Imbalanced Data Credit Card Fraud Detection Random Forest Classifier

February 24, 2023

February 15, 2023

Dual Graph Multitask Framework for Imbalanced Delivery Time Estimation
Lei Zhang, Mingliang Wang, Xin Zhou, Xingyu Wu, Yiming Cao, Yonghui Xu, Lizhen Cui, Zhiqi Shen
Multi Task Imbalanced Data Dual Graph Parcel Delivery

February 14, 2023

Generative Oversampling for Imbalanced Data via Majority-Guided VAE
Qingzhong Ai, Pengyun Wang, Lirong He, Liangjian Wen, Lujia Pan, Zenglin Xu
Model Overfitting Imbalanced Data Finite Sample Minority Sample Sampling Algorithm Generative Sampling

January 21, 2023

Pre-text Representation Transfer for Deep Learning with Limited Imbalanced Data : Application to CT-based COVID-19 Detection
Fouzia Altaf, Syed M. S. Islam, Naeem K. Janjua, Naveed Akhtar
Deep Learning Transfer Learning Application Proficiency Imbalanced Data COVID 19 Detection Medical Image Data Transferable Representation Pre Trained Text

January 1, 2023

Neural Collapse in Deep Linear Networks: From Balanced to Imbalanced Data
Hien Dang, Tho Tran, Stanley Osher, Hung Tran-The, Nhat Ho, Tan Nguyen
Neural Collapse Imbalanced Data Deep Linear Global Minimum Balanced Approach Layer Output

December 15, 2022

Interpretable ML for Imbalanced Data
Damien A. Dablain, Colin Bellinger, Bartosz Krawczyk, David W. Aha, Nitesh V. Chawla
Deep Learning Model Deep Network Imbalanced Data Opaque Machine Learning Deep Imbalanced

November 19, 2022

Quantifying Human Bias and Knowledge to guide ML models during Training
Hrishikesh Viswanath, Andrey Shor, Yoshimasa Kitaguchi
Training Data Machine Learning Model Knowledge Based Imbalanced Data Crowdsourcing Context Length Bias Quantifying Bias Initial Weight

Imbalanced Data

Papers

Invariant Feature Regularization for Fair Face Recognition

A Review of Machine Learning Techniques in Imbalanced Data and Future Trends

Survey on Imbalanced Data, Representation Learning and SEP Forecasting

An Investigation Into Race Bias in Random Forest Models Based on Breast DCE-MRI Derived Radiomics Features

Neural Collapse for Unconstrained Feature Model under Cross-entropy Loss with Imbalanced Data

From SMOTE to Mixup for Deep Imbalanced Classification

Distributed Dual Coordinate Ascent with Imbalanced Data on a General Tree Network

The Effect of Balancing Methods on Model Behavior in Imbalanced Classification Problems

Entropy-Aware Similarity for Balanced Clustering: A Case Study with Melanoma Detection

Towards Understanding How Data Augmentation Works with Imbalanced Data

Evaluating XGBoost for Balanced and Imbalanced Data: Application to Fraud Detection

Credit Card Fraud Detection Using Enhanced Random Forest Classifier for Imbalanced Data

Data Augmentation with GAN increases the Performance of Arrhythmia Classification for an Unbalanced Dataset

Scalable Unbalanced Sobolev Transport for Measures on a Graph

Dual Graph Multitask Framework for Imbalanced Delivery Time Estimation

Generative Oversampling for Imbalanced Data via Majority-Guided VAE

Pre-text Representation Transfer for Deep Learning with Limited Imbalanced Data : Application to CT-based COVID-19 Detection

Neural Collapse in Deep Linear Networks: From Balanced to Imbalanced Data

Interpretable ML for Imbalanced Data

Quantifying Human Bias and Knowledge to guide ML models during Training