Dynamic Early Stopping for Naive Bayes
Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence. Pages 2082-2087.
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.
@inproceedings{ijcai2016p297,
title = {Dynamic Early Stopping for Naive Bayes},
author = {Verachtert, Aäron and Blockeel, Hendrik and Davis, Jesse},
booktitle = {Proceedings of the Twenty-Fifth International Joint Conference on
Artificial Intelligence, {IJCAI-16}},
publisher = {AAAI Press / International Joint Conferences on Artificial Intelligence},
editor = {Subbarao Kambhampati},
pages = {2082--2087},
year = {2016},
month = {7},
url = {https://ijcai.org/proceedings/2016/297},
}
TY - CPAPER TI - Dynamic Early Stopping for Naive Bayes AU - Verachtert, Aäron AU - Blockeel, Hendrik AU - Davis, Jesse T2 - Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence, IJCAI-16 PB - AAAI Press / International Joint Conferences on Artificial Intelligence PY - 2016 SP - 2082 EP - 2087 UR - https://ijcai.org/proceedings/2016/297 ER -Copied
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