Dynamic Early Stopping for Naive Bayes / 2082
Aäron Verachtert, Hendrik Blockeel, Jesse Davis
Energy efficiency is a concern for any software running on mobile devices. As such software employs machine-learned models to make predictions, this motivates research on efficiently executable models. In this paper, we propose a variant of the widely used Naive Bayes (NB) learner that yields a more efficient predictive model. In contrast to standard NB, where the learned model inspects all features to come to a decision, or NB with feature selection, where the model uses a fixed subset of the features, our model dynamically determines, on a case-by-case basis, when to stop inspecting features. We show that our approach is often much more efficient than the current state of the art, without loss of accuracy.