caa a22 4500 605478341 CHVBK 20210128100405.0 cr unu---uuuuu 210128e20150901xx s 000 0 eng 10.1007/s10994-015-5504-1 doi (NATIONALLICENCE)springer-10.1007/s10994-015-5504-1 Optimised probabilistic active learning (OPAL) [Elektronische Daten] For fast, non-myopic, cost-sensitive active classification [Georg Krempl, Daniel Kottke, Vincent Lemaire] In contrast to ever increasing volumes of automatically generated data, human annotation capacities remain limited. Thus, fast active learning approaches that allow the efficient allocation of annotation efforts gain in importance. Furthermore, cost-sensitive applications such as fraud detection pose the additional challenge of differing misclassification costs between classes. Unfortunately, the few existing cost-sensitive active learning approaches rely on time-consuming steps, such as performing self-labelling or tedious evaluations over samples. We propose a fast, non-myopic, and cost-sensitive probabilistic active learning approach for binary classification. Our approach computes the expected reduction in misclassification loss in a labelling candidate's neighbourhood. We derive and use a closed-form solution for this expectation, which considers the possible values of the true posterior of the positive class at the candidate's position, its possible label realisations, and the given labelling budget. The resulting myopic algorithm runs in the same linear asymptotic time as uncertainty sampling, while its non-myopic counterpart requires an additional factor of $$O(m \cdot \log m)$$ O ( m · log m ) in the budget size. The experimental evaluation on several synthetic and real-world data sets shows competitive or better classification performance and runtime, compared to several uncertainty sampling- and error-reduction-based active learning strategies, both in cost-sensitive and cost-insensitive settings. The Author(s), 2015 Active learning nationallicence Non-myopic nationallicence Cost-sensitive nationallicence Unequal misclassification costs nationallicence Misclassification loss nationallicence Imbalanced data nationallicence Uncertainty sampling nationallicence Error reduction nationallicence Krempl Georg KMD Lab, University Magdeburg, Magdeburg, Germany aut Kottke Daniel KMD Lab, University Magdeburg, Magdeburg, Germany aut Lemaire Vincent Orange Labs, Lannion, France aut Machine Learning Springer US; http://www.springer-ny.com 100/2-3(2015-09-01), 449-476 0885-6125 100:2-3<449 2015 100 10994 https://doi.org/10.1007/s10994-015-5504-1 text/html Onlinezugriff via DOI BK010053 XK010053 XK010000 Metadata rights reserved Springer special CC-BY-NC licence nationallicence 1 research-article jats NATIONALLICENCE NATIONALLICENCE NL-springer NATIONALLICENCE 856 40 https://doi.org/10.1007/s10994-015-5504-1 text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Krempl Georg KMD Lab, University Magdeburg, Magdeburg, Germany aut NATIONALLICENCE 700 1- Kottke Daniel KMD Lab, University Magdeburg, Magdeburg, Germany aut NATIONALLICENCE 700 1- Lemaire Vincent Orange Labs, Lannion, France aut NATIONALLICENCE 773 0- Machine Learning Springer US; http://www.springer-ny.com 100/2-3(2015-09-01), 449-476 0885-6125 100:2-3<449 2015 100 10994