Quelltext der Seite Batch query strategies for one-class active learning

{{Vortrag
|vortragender=Dennis Vetter
|email=upddg@student.kit.edu
|vortragstyp=Proposal
|betreuer=Holger Trittenbach
|termin=Institutsseminar/2018-10-26
|kurzfassung=One-class classifiers learn to distinguish normal objects from outliers. These classifiers are therefore suitable for strongly imbalanced class distributions with only a small fraction of outliers. Extensions of one-class classifiers make use of labeled samples to improve classification quality. As this labeling process is often time-consuming, one may use active learning methods to detect samples where obtaining a label from the user is worthwhile, with the goal of reducing the labeling effort to a fraction of the original data set. In the case of one-class active learning this labeling process consists of sequential queries, where the user labels one sample at a time. While batch queries where the user labels multiple samples at a time have potential advantages, for example parallelizing the labeling process, their application has so far been limited to binary and multi-class classification. In this thesis we explore whether batch queries can be used for one-class classification. We strive towards a novel batch query strategy for one-class classification by applying concepts from multi-class classification to the requirements of one-class active learning.
}}