{"type":"ScholarlyArticle","title":"Dynamic Early Stopping for Naive Bayes","authors":[{"name":"Aäron Verachtert","slug":"aaron-verachtert","given":"Aäron","family":"Verachtert","affiliation":""},{"name":"Hendrik Blockeel","slug":"hendrik-blockeel","given":"Hendrik","family":"Blockeel","affiliation":""},{"name":"Jesse Davis","slug":"jesse-davis","given":"Jesse","family":"Davis","affiliation":""}],"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.","doi":null,"url":"https://ijcai.org/proceedings/2016/297","pdf":"https://ijcai.org/proceedings/2016/0297.pdf","markdown":"https://ijcai.org/proceedings/2016/297.md","bibtex":"https://ijcai.org/proceedings/2016/bibtex/297","volume":{"slug":"2016","title":"Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2016","url":"https://ijcai.org/proceedings/2016/","year":2016,"location":"New York, NY, USA","dates":"9–15 July 2016","editors":"Subbarao Kambhampati","isbn":"978-1-57735-770-4; 978-1-57735-771-1","publisher":"AAAI Press / International Joint Conferences on Artificial Intelligence","doi_prefix":""},"section":null,"track":"","number":297,"pages":{"first":2082,"last":2087},"keywords":[],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}