Active Learning with Scarcely Labeled Data via Bias Variance Reduction

In many occasions in real life, we are faced with the problem of classification of partially labeled data, or semi-supervised learning. We consider the special case of scarcely labeled data or when the labeled data is insufficient, and present a principled method which implements active learning in scarcely labeled data to enhance the performance of the learner. This method is based on the recent bias variance decomposition work for a 0-1 loss function. We focus on bias and variance reduction to reduce 0-1 loss by first selecting a random pool from the unlabeled data, and then using the mostinformative instances from that pool to reduce the variance, bias, and thereby overall loss of the learner via active learning. Our empirical results show that this technique can decrease the loss of the learner significantly.