165 citations · 201 across the 18 of their papers we have counts for
4 papers · 1 filter
Interpretable ML for Imbalanced Data
Damien A. Dablain, Colin Bellinger, Bartosz Krawczyk +2
Deep learning models are being increasingly applied to imbalanced data in high stakes fields such as medicine, autonomous driving, and intelligence analysis. Imbalanced data compou…
Towards A Holistic View of Bias in Machine Learning: Bridging Algorithmic Fairness and Imbalanced Learning
Damien Dablain, Bartosz Krawczyk, Nitesh Chawla
Machine learning (ML) is playing an increasingly important role in rendering decisions that affect a broad range of groups in society. ML models inform decisions in criminal justic…
Efficient Augmentation for Imbalanced Deep Learning
Damien Dablain, Colin Bellinger, Bartosz Krawczyk +1
Deep learning models tend to memorize training data, which hurts their ability to generalize to under-represented classes. We empirically study a convolutional neural network's int…
A survey on learning from imbalanced data streams: taxonomy, challenges, empirical study, and reproducible experimental framework
Gabriel Aguiar, Bartosz Krawczyk, Alberto Cano
Class imbalance poses new challenges when it comes to classifying data streams. Many algorithms recently proposed in the literature tackle this problem using a variety of data-leve…