Imbalanced Learning

Imbalanced learning tackles the challenge of building accurate machine learning models when training data contains a disproportionate number of samples from different classes. Current research focuses on developing improved algorithms and model architectures, such as cost-sensitive learning, ensemble methods (like balanced random forests), and novel data augmentation techniques (e.g., variations of SMOTE and Mixup), to mitigate the bias towards majority classes and improve minority class prediction. This field is crucial for numerous real-world applications where imbalanced data is prevalent, including fraud detection, medical diagnosis, and rare event prediction, impacting the reliability and fairness of machine learning systems.

Papers

July 13, 2022

Towards A Holistic View of Bias in Machine Learning: Bridging Algorithmic Fairness and Imbalanced Learning
Damien Dablain, Bartosz Krawczyk, Nitesh Chawla
Machine Learning Absolute Stance Bias Class Imbalance Group Fairness Algorithmic Fairness Model Fairness Imbalanced Learning Holistic Perception

May 24, 2022

Deep Reinforcement Learning for Multi-class Imbalanced Training
Jenny Yang, Rasheed El-Bouri, Odhran O'Donoghue, Alexander S. Lachapelle, Andrew A. S. Soltan, David A. Clifton
Reinforcement Learning Deep Reinforcement Learning Imbalanced Learning Double Deep Q Learning Class Imbalanced Learning Balanced Classification

April 20, 2022

Neurochaos Feature Transformation and Classification for Imbalanced Learning
Deeksha Sethi, Nithin Nagaraj, Harikrishnan N B
Classification Code Decision Tree Imbalanced Data Imbalanced Learning

April 19, 2022

Neural Collapse Inspired Attraction-Repulsion-Balanced Loss for Imbalanced Learning
Liang Xie, Yibo Yang, Deng Cai, Xiaofei He
Deep Learning Model Large Scale Class Imbalance Neural Collapse Imbalanced Learning

March 17, 2022

Inducing Neural Collapse in Imbalanced Learning: Do We Really Need a Learnable Classifier at the End of Deep Neural Network?
Yibo Yang, Shixiang Chen, Xiangtai Li, Liang Xie, Zhouchen Lin, Dacheng Tao
Deep Neural Network Neural Collapse Linear Classifier Annotated End State Imbalanced Learning Learning Classifier Frame Classification

January 28, 2022

Adversarial Examples for Good: Adversarial Examples Guided Imbalanced Learning
Jie Zhang, Lei Zhang, Gang Li, Chao Wu
Machine Learning Model Adversarial Example Imbalanced Learning Biased Decision First Benchmark Dataset

November 24, 2021

Handling Inter-class and Intra-class Imbalance in Class-imbalanced Learning
Zhining Liu, Pengfei Wei, Zhepei Wei, Boyang Yu, Jing Jiang, Wei Cao, Jiang Bian, Yi Chang
Class Imbalance Imbalanced Learning Class Imbalanced Learning Inter Class