1 citations · 1 across the 4 of their papers we have counts for
4 papers
Analyzing decision tree bias towards the minority class
Nathan Phelps, Daniel J. Lizotte, Douglas G. Woolford
There is a widespread and longstanding belief that machine learning models are biased towards the majority class when learning from imbalanced binary response data, leading them to…
Challenges in the calibration of tree-based models for imbalanced classification
Nathan Phelps, Daniel J. Lizotte, Douglas G. Woolford
When using machine learning for imbalanced binary classification problems, it is common to subsample the majority class to create a (more) balanced training dataset. This biases th…
Using Platt's scaling for calibration after undersampling -- limitations and how to address them
Nathan Phelps, Daniel J. Lizotte, Douglas G. Woolford
When modelling data where the response is dichotomous and highly imbalanced, response-based sampling where a subset of the majority class is retained (i.e., undersampling) is often…
Reinforcement learning in large, structured action spaces: A simulation study of decision support for spinal cord injury rehabilitation
Nathan Phelps, Stephanie Marrocco, Stephanie Cornell +2
Reinforcement learning (RL) has helped improve decision-making in several applications. However, applying traditional RL is challenging in some applications, such as rehabilitation…