3 papers
cs.LG2022
High Per Parameter: A Large-Scale Study of Hyperparameter Tuning for Machine Learning Algorithms
Moshe Sipper
Hyperparameters in machine learning (ML) have received a fair amount of attention, and hyperparameter tuning has come to be regarded as an important step in the ML pipeline. But ju…
cs.LG2022
Combining Deep Learning with Good Old-Fashioned Machine Learning
Moshe Sipper
We present a comprehensive, stacking-based framework for combining deep learning with good old-fashioned machine learning, called Deep GOld. Our framework involves ensemble selecti…
cs.NE2022
Automatically Balancing Model Accuracy and Complexity using Solution and Fitness Evolution (SAFE)
Moshe Sipper, Jason H. Moore, Ryan J. Urbanowicz
When seeking a predictive model in biomedical data, one often has more than a single objective in mind, e.g., attaining both high accuracy and low complexity (to promote interpreta…