General MC: estimating boundary of positive class from small positive data

Single-class classification (SCC) seeks to distinguish one class of data from the universal set of multiple classes. We propose a SCC method called general MC that estimates an accurate classification boundary of positive class from small positive data using the distribution of unlabeled data. Our theoretical and empirical analyses show that, as long as the distribution of unlabeled data is not highly skewed in the feature space, general MC significantly outperforms other recent SCC methods when the positive data set is highly under-sampled.