We discuss Empirical Risk Minimization approach in conjunction with one-class classification method to learn classifiers for biased Positive Unlabeled (PU) data. For such data, probability that an observation from a positive class is labeled may depend on its features. The proposed method extends Variational Autoencoder for PU data (VAE-PU) introduced in [16] by proposing another estimator of a theoretical risk of a classifier to be minimized, which has important advantages over the previous proposal. This is based on one-class classification approach using generated pseudo-observations, which turns out to be an effective method of detecting positive observations among unlabeled ones. The proposed method leads to more precise estimation of the theoretical risk than the previous proposal. Experiments performed on real data sets show that the proposed VAE-PU+OCC algorithm works very promisingly in comparison to its competitors such as the original VAE-PU, SAR-EM and LBE methods in terms of accuracy and F1 score. The advantage is especially strongly pronounced for small labeling frequencies.