Reinforced Meta Active Learning
- URL: http://arxiv.org/abs/2203.04573v1
- Date: Wed, 9 Mar 2022 08:36:54 GMT
- Title: Reinforced Meta Active Learning
- Authors: Michael Katz, Eli Kravchik
- Abstract summary: We present an online stream-based meta active learning method which learns on the fly an informativeness measure directly from the data.
The method is based on reinforcement learning and combines episodic policy search and a contextual bandits approach.
We demonstrate on several real datasets that this method learns to select training samples more efficiently than existing state-of-the-art methods.
- Score: 11.913086438671357
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: In stream-based active learning, the learning procedure typically has access
to a stream of unlabeled data instances and must decide for each instance
whether to label it and use it for training or to discard it. There are
numerous active learning strategies which try to minimize the number of labeled
samples required for training in this setting by identifying and retaining the
most informative data samples. Most of these schemes are rule-based and rely on
the notion of uncertainty, which captures how small the distance of a data
sample is from the classifier's decision boundary. Recently, there have been
some attempts to learn optimal selection strategies directly from the data, but
many of them are still lacking generality for several reasons: 1) They focus on
specific classification setups, 2) They rely on rule-based metrics, 3) They
require offline pre-training of the active learner on related tasks. In this
work we address the above limitations and present an online stream-based meta
active learning method which learns on the fly an informativeness measure
directly from the data, and is applicable to a general class of classification
problems without any need for pretraining of the active learner on related
tasks. The method is based on reinforcement learning and combines episodic
policy search and a contextual bandits approach which are used to train the
active learner in conjunction with training of the model. We demonstrate on
several real datasets that this method learns to select training samples more
efficiently than existing state-of-the-art methods.
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