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Title: Dynamic early stopping for naive Bayes
Other Titles: Dynamic early stopping for naive Bayes
Authors: Verachtert, AƤron ×
Blockeel, Hendrik
Davis, Jesse #
Issue Date: Jul-2016
Publisher: AAAI Press
Host Document: Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence pages:2082-2088
Conference: International Joint Conference on Artificial Intelligence edition:25th location:New York, NY date:9 - 15 July 2016
Abstract: 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.
Publication status: published
KU Leuven publication type: IC
Appears in Collections:Informatics Section
× corresponding author
# (joint) last author

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