To address the problem that quality and quantity of training samples directly affect accuracy of oneclass classification (OCC) methods, this paper investigates the use of active learning in selection of training samples of target class (positive samples) for improving the performance of OCC, by taking positive and unlabeled learning (PUL) as an example. PUL is first trained with sufficient training samples selected randomly until a stable accuracy is reached. Most informative positive and negative training samples collected by using active learning strategy are then added in PUL classification. The experimental results show that after sufficient samples are used for classification, the use of positive samples selected by using active learning still outperformed that using sufficient positive samples selected randomly. PUL classification by adding both positive and negative samples outperformed that by adding positive samples only. Furthermore, PUL classification using positive samples after removal of redundant positive samples from those directly selected by active learning obtains accuracy comparable to that using more positive samples directly selected by active learning, whereas less samples are needed in the case that redundant samples are removed. This study demonstrates that selecting and adding samples by active learning provides a more effective way of improving accuracy for OCC.