$α$QBoost: An Iteratively Weighted Adiabatic Trained Classifier
This work addresses the need for more stable and efficient ensemble models in applications requiring explainability and fast inference, though it appears incremental as an implementation improvement over existing adiabatic training methods.
The paper tackles the problem of improving ensemble model performance and stability by introducing αQBoost, an adiabatically-trained classifier that reduces variance and bias. The result is higher performance on unseen data with fewer classifiers, faster convergence, and improved stability.
A new implementation of an adiabatically-trained ensemble model is derived that shows significant improvements over classical methods. In particular, empirical results of this new algorithm show that it offers not just higher performance, but also more stability with less classifiers, an attribute that is critically important in areas like explainability and speed-of-inference. In all, the empirical analysis displays that the algorithm can provide an increase in performance on unseen data by strengthening stability of the statistical model through further minimizing and balancing variance and bias, while decreasing the time to convergence over its predecessors.