Batch query strategies for one-class active learning
|Termin||Fr 26. Oktober 2018|
|Kurzfassung||One-class classifiers learn to distinguish normal objects from outliers. Such classifiers are therefore suitable for strongly imbalanced class distributions with only a small fraction of outliers. There are extensions of one-class classifiers that make use of labeled samples to improve classification quality. As the labeling must often be done by an expert, obtaining these labeled samples can be very expensive and time-consuming. To reduce these labeling efforts, one may rely on active learning methods that 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.
This labeling process can be performed with sequential queries, where the user is asked to label one sample at a time, or batch queries, where the user is asked to label multiple samples at once. Research on Active Learning for one-class classifiers has focused on sequential queries. While batch queries have potential advantages, like enabling a parallelization of the labeling process, their application has so far been limited to binary and multi-class classification.
We propose a new batch query strategy for one-class classification by applying concepts from multi-class classification to the special requirements of one-class active learning.