Semi-supervised classification with active query selection

Labeled samples are crucial in semi-supervised classification, but which samples should we choose to be the labeled samples? In other words, which samples, if labeled, would provide the most information? We propose a method to solve this problem. First, we give each unlabeled examples an initial class label using unsupervised learning. Then, by maximizing the mutual information, we choose the samples with most information to be user-specified labeled samples. After that, we run semi-supervised algorithm with the user-specified labeled samples to get the final classification. Experimental results on synthetic data show that our algorithm can get a satisfying classification results with active query selection.