To Actively Initialize Active Learning

To Actively Initialize Active Learning

Yang, Yazhou (National University of Defense Technology)
Loog, M. (TU Delft Pattern Recognition and Bioinformatics; University of Copenhagen)

2022

Though much effort has been spent on designing new active learning algorithms, little attention has been paid to the initialization problem of active learning, i.e., how to find a set of labeled samples which contains at least one instance per category. This work identifies the initialization of active learning as a separate and novel research problem, reviews existing methods that can be adapted to be used for this task and, in addition, proposes a new active initialization criterion: the Nearest Neighbor Criterion. Experiments on 16 benchmark datasets verify that the novel method often finds an initialization set with fewer queried samples than other methods do.

active initialization
active learning
minimum nearest neighbor distance
nearest neighbor criterion

http://resolver.tudelft.nl/uuid:6e229bbf-af89-4ae7-817b-b7989676c94b

https://doi.org/10.1016/j.patcog.2022.108836

2023-07-01

0031-3203

Pattern Recognition, 131

Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.

Institutional Repository

journal article

© 2022 Yazhou Yang, M. Loog