Estimating the Probability of a Class at a Point by the Approximation of One Discriminant Function
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We propose a method for estimating the posterior probability of a class at a given point by approximating a discriminant function that takes a zero value at this point. The approximation is based on a supervised training set. Posterior probabilities of classes allow the classification problem to be solved simultaneously for different criteria and different costs of classification errors. The method is based on choosing such a ratio of the costs of classification errors in the construction of an approximation to the discriminant function that the approximation takes the zero value at a given point. We give a model example and an example with real data from the field of medical diagnostics.
Keywordsmachine learning classification evaluating posterior probability of a class approximation of a discriminant function disease diagnostics
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