2026-07-072026-07-07http://salesiana.dossiersoluciones.com/handle/123456789/169514This paper generalizes the traditional statistical concept of prediction intervals for arbitrary probability density functions in high-dimensional feature spaces by introducing significance level distributions, which provides interval-independent probabilities for continuous random variables. The advantage of the transformation of a probability density function into a significance level distribution is that it enables one-class classification or outlier detection in a direct manner.13 pages, 3 figuresComputer Vision and Pattern RecognitionArtificial IntelligenceMachine Learningtext