Uniform Object Generation for Optimizing One-class Classifiers
David M.J. Tax, Robert P.W. Duin;
2(Dec):155-173, 2001.
Abstract
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.
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