Tim Erdmann, Stefan Zecevic, et al.
ACS Spring 2024
We previously discussed how classifiers based on logistic regression and decision trees can be used for predicting the class of an observation. Unfortunately, when such classifiers are trained on a dataset in which one of the response classes is rare, they can underestimate the probability of observing a rare event — the greater the imbalance, the greater this small-sample bias. This month, we illustrate how to mitigate the negative effect of class imbalance on the training of classifiers.
Tim Erdmann, Stefan Zecevic, et al.
ACS Spring 2024
Divyansh Jhunjhunwala, Neharika Jali, et al.
ISIT 2024
Yidi Wu, Thomas Bohnstingl, et al.
ICML 2025
Oscar Sainz, Iker García-ferrero, et al.
ACL 2024