Query Selection via Weighted Entropy in Graph-Based Semi-supervised Classification

There has recently been a large effort in using unlabeled data in conjunction with labeled data in machine learning. Semi-supervised learning and active learning are two well-known techniques that exploit the unlabeled data in the learning process. In this work, the active learning is used to query a label for an unlabeled data on top of a semisupervised classifier. This work focuses on the query selection criterion. The proposed criterion selects the example for which the label change results in the largest pertubation of other examples’ label. Experimental results show the effectiveness of the proposed query selection criterion in comparison to existing techniques.