Abstract
The majority of classification algorithms are developed for the standard
situation in which it is assumed that the examples in the training
set come from the same distribution as that of the target population,
and that the cost of misclassification into different classes are
the same. However, these assumptions are often violated in real
world settings. For some classification methods, this can often
be taken care of simply with a change of threshold; for others,
additional effort is required. In this paper, we explain why the
standard support vector machine is not suitable for the nonstandard
situation, and introduce a simple procedure for adapting the support
vector machine methodology to the nonstandard situation. Theoretical
justification for the procedure is provided. Simulation study illustrates
that the modified support vector machine significantly improves
upon the standard support vector machine in the nonstandard situation.
The computational load of the proposed procedure is the same as
that of the standard support vector machine. The procedure reduces
to the standard support vector machine in the standard situation.
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