Single-class classifier learning using neural networks: an application to the prediction of mineral deposits

Single-class classifier learning is the problem of learning a classifier from a set of training examples in which only examples of the target class are present. Most existing approaches to this problem are based on density estimation and hence suffer from the usual problems associated with estimating probability densities in high dimensional spaces. This paper describes how feedforward neural networks can be used to learn a classifier from a dataset consisting of (labeled) examples of the target class (positive examples) together with a corpus of unlabeled (positive and negative) examples. An application of the technique to the prediction of mineral deposit location is provided, and empirical results are presented.