Toward optimal probabilistic active learning using a Bayesian approach

Gathering labeled data to train well-performing machine learning models is one of the critical challenges in many applications. Active learning aims at reducing the labeling costs by an efficient and effective allocation of costly labeling resources. In this article, we propose a decision-theoretic...

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Bibliographic Details
Published in:Machine learning 2021-06, Vol.110 (6), p.1199-1231
Main Authors: Kottke, Daniel, Herde, Marek, Sandrock, Christoph, Huseljic, Denis, Krempl, Georg, Sick, Bernhard
Format: Article
Language:English
Subjects:
Active learning
Artificial Intelligence
Bayesian analysis
Computer Science
Control
Decision theory
Labeling
Machine Learning
Mechatronics
Natural Language Processing (NLP)
Robotics
Simulation and Modeling
Special Issue of the ECML PKDD 2021 Journal Track
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Summary:Gathering labeled data to train well-performing machine learning models is one of the critical challenges in many applications. Active learning aims at reducing the labeling costs by an efficient and effective allocation of costly labeling resources. In this article, we propose a decision-theoretic selection strategy that (1) directly optimizes the gain in misclassification error, and (2) uses a Bayesian approach by introducing a conjugate prior distribution to determine the class posterior to deal with uncertainties. By reformulating existing selection strategies within our proposed model, we can explain which aspects are not covered in current state-of-the-art and why this leads to the superior performance of our approach. Extensive experiments on a large variety of datasets and different kernels validate our claims.
ISSN:0885-6125
1573-0565
DOI:10.1007/s10994-021-05986-9