The data classification apparatus and method is adapted to high-dimensional classification problems and provide a universal measure of confidence that is valid under the iid assumption. The method employs the assignment of strangeness values to classification sets constructed using classification training examples and an unclassified example. The strangeness values of p-values are compared to identify the classification set containing the most likely potential classification for the unclassified example. The measure of confidence is then computed on the basis of the strangeness value of the classification set containing the second most likely potential classification.