Uniform object generation for optimizing one-class classifiers
In one-class classification, one class of data, called the target class, has to be distinguished from the rest of the feature space. It is assumed that only examples of the target class are available. This classifier has to be constructed such that objects not originating from the target set, by definition outlier objects, are not classified as target objects. In previous research the support vector data description (SVDD) is proposed to solve the problem of one-class classification. It models a hypersphere around the target set, and by the introduction of kernel functions, more flexible descriptions are obtained. In the original optimization of the SVDD, two parameters have to be given beforehand by the user. To automatically optimize the values for these parameters, the error on both the target and outlier data has to be estimated. Because no outlier examples are available, we propose a method for generating artificial outliers, uniformly distributed in a hypersphere. An (relative) efficient estimate for the volume covered by the one-class classifiers is obtained, and so an estimate for the outlier error. Results are shown for artificial data and for real world data.
one-class classification support vector classifiers novelty detection outlier detection