A Benchmark and Comparison of Active Learning for Logistic Regression

Yazhou Yang, Marco Loog

Published 2016-11-25Version 1

Various active learning methods based on logistic regression have been proposed. In this paper, we investigate seven state-of-the-art strategies, present an extensive benchmark, and provide a better understanding of their underlying characteristics. Experiments are carried out both on 3 synthetic datasets and 43 real-world datasets, providing insights into the behaviour of these active learning methods with respect to classification accuracy and their computational cost.