9 citations · 26 across the 8 of their papers we have counts for
5 papers · 1 filter
The Hidden Influence of Latent Feature Magnitude When Learning with Imbalanced Data
Damien A. Dablain, Nitesh V. Chawla
Machine learning (ML) models have difficulty generalizing when the number of training class instances are numerically imbalanced. The problem of generalization in the face of data…
Towards Understanding How Data Augmentation Works with Imbalanced Data
Damien A. Dablain, Nitesh V. Chawla
Data augmentation forms the cornerstone of many modern machine learning training pipelines; yet, the mechanisms by which it works are not clearly understood. Much of the research o…
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…