BATCH MODE ACTIVE LEARNING FOR GRAPH-BASED SEMI-SUPERVISED LEARNING

Abstract

In semi-supervised learning, when the number of data samples with class label information is very small, information from unlabeled data is utilized in the learning process. Many semi-supervised learning methods have been presented and have exhibited competent performance. Active learning also 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. Instead of acquiring class label information of one unlabeled data sample at a time, we obtain information about several data samples at once, reducing time complexity while preserving the beneficial effects of active learning. Experimental results demonstrate the improved performance of the proposed method.