P-Values for Classification
Lutz Dümbgen, Igl, Bernd-Wolfgang, Axel Munk · 2008
Abstract: Let (X,Y) be a random variable consisting of an observed feature vector X ∈ X and an unobserved class label Y ∈ {1,2,...,L} with unknown joint distribution. In addition, let D be a training data set consisting of n completely observed independent copies of (X,Y). Usual classification procedures provide point predictors (classifiers) Y (X, D) of Y or estimate the conditional distribution of Y given X. In order to quantify the certainty of classifying X we propose to construct for each θ = 1,2,..., L a p-value πθ(X, D) for the null hypothesis that Y = θ, treating Y temporarily as a fixed parameter. In other words, the point predictor Y (X, D) is replaced with a prediction region for Y with a certain confidence. We argue that (i) this approach is advantageous over traditional approaches and (ii) any reasonable classifier can be modified to yield nonparametric p-values. We discuss issues such as optimality, single use and multiple use validity, as well as computational and graphical aspects.