Graph-Based Batch Mode Active Learning

  • Cheong Hee ParkEmail author
Conference paper
Part of the Springer Proceedings in Complexity book series (SPCOM)


Active 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.


Active learning Batch mode active learning Label propagation Semi-supervised learning 


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Copyright information

© Springer Science+Business Media Dordrecht 2013

Authors and Affiliations

  1. 1.Department of Computer Science and EngineeringChungnam National UniversityDaejeonKorea

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