One-Class Classification by Combining Density and Class Probability Estimation

One-class classification has important applications such as outlier and novelty detection. It is commonly tackled using density estimation techniques or by adapting a standard classification algorithm to the problem of carving out a decision boundary that describes the location of the target data. In this paper we investigate a simple method for one-class classification that combines the application of a density estimator, used to form a reference distribution with the induction of a standard model for class probability estimation. In this method, the reference, distribution is used to generate artificial data that is employed to form a second, artificial class. In conjunction with the target class, this artificial class is the basis for a standard two-class learning problem. We explain how the density function of the reference distribution can be combined with the class probability estimates obtained in this way to form a adjusted estimate of the density function of the target class. Using UCI datasets, and data from a typist recognition problem we show that the combined model, consisting of both a density estimator and a class probability estimator, call improve on using either component technique alone when used for one-class classification. We also compare the method to one-class classification using support vector machines.