---
title: "Dynamic Early Stopping for Naive Bayes"
authors:
  - "Aäron Verachtert"
  - "Hendrik Blockeel"
  - "Jesse Davis"
doi: ""
url: "https://ijcai.org/proceedings/2016/297"
pdf: "https://ijcai.org/proceedings/2016/0297.pdf"
venue: "Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence"
year: 2016
pages: "2082-2087"
track: ""
keywords:
---

# Dynamic Early Stopping for Naive Bayes

Aäron Verachtert, Hendrik Blockeel, Jesse Davis

*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.