Toward optimal probabilistic active learning using a Bayesian approach
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In: Machine Learning Volume 110 / Issue 6 (2021-05-04) , S. 1199-1231; eissn:1573-0565
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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.
@article{doi:10.17170/kobra-202107054244, author ={Kottke, Daniel and Herde, Marek and Sandrock, Christoph and Huseljic, Denis and Krempl, Georg and Sick, Bernhard}, title ={Toward optimal probabilistic active learning using a Bayesian approach}, keywords ={004 and Maschinelles Lernen and Klassifikation and Bayes-Verfahren and Daten}, copyright ={http://creativecommons.org/licenses/by/4.0/}, language ={en}, journal ={Machine Learning}, year ={2021-05-04} }