Multi-Class Probabilistic Active Learning

by Daniel Kottke, Georg Krempl, Dominik Lang, Johannes Teschner, Myra Spiliopoulou This work addresses active learning for multi-class classification. Active learning algorithms optimize classifier performance by successively selecting the most beneficial instances from a pool of unlabeled instances to be labeled by an oracle. In this work, we study the influence of the following factors […]

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Active Selection of Difficult Classes

by Marianne Stecklina, Tuan Phan Minh, Tim Sabsch, Cornelius Styp von Rekowski, Daniel Kottke, Georg Krempl, Matthias Deliano, Myra Spiliopoulou. In multi-class classification, datasets often contain both classes that can be easily separated from others and classes that require many data to learn an expressive decision boundary from. In active class selection (ACS), the main […]

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Temporal Aspects in Stream Active Learning

by Daniel Kottke, Georg Krempl and Myra Spiliopoulou. Classification systems use data (consisting of instances and class labels) to learn a model that predicts the unknown class label of unseen instances. In contrast to the very fast and cheap generation of instances due to big data and connected systems in everyones life, the labeling of […]

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Probabilistic Active Learning in Data Streams

by Daniel Kottke, Georg Krempl, Myra Spiliopoulou.  In recent years, stream-based active learning has become an intensively investigated research topic. In this work, we propose a new algorithm for stream-based active learning that decides immediately whether to acquire a label (selective sampling). It uses Probabilistic Active Learning (PAL) to measure the spatial usefulness of each […]

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