Abstract
AbstractActive learning aims to overcome the shortage of labeled data by obtaining class labels for some selected unlabeled data from experts. However, the selection process for the most informative unlabeled data samples can be demanding when the search is performed over a large set of unlabeled data. In this paper, we propose a method for batch mode active learning in graph-based semi-supervised learning. By acquiring class label information about several unlabeled data samples at a time, the proposed method reduces time complexity while preserving the beneficial effects of active learning. Experimental results demonstrate the improved performance of the proposed method.KeywordsActive learningBatch mode active learningLabel propagationSemi-supervised learning
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