2020/01/31 by Louise Kimpton, Kimpton, Louise, Peter Challenor +3
Computer Science · Mathematics · Physics and Astronomy · #Advanced Multi-Objective Optimization Algorithms #Artificial intelligence #Bernoulli's principle #Classifier (UML) #Computer science #FOS: Computer and information sciences #Gaussian Processes and Bayesian Inference #Latent variable #Logistic regression #Machine learning #Mathematics #Methodology (stat.ME) #Pattern recognition (psychology) #Scientific Research and Discoveries #Statistics #stat.ME
paper · pdf · doi:10.48550/arxiv.2002.00010
published in arXiv (Cornell University) (Cornell University) · arXiv admin note: substantial text overlap with arXiv:1901.07413
arxiv created 2020/01/31 · openalex publication_date 2020/01/31 · arxiv updated 2020/02/04 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Classification is a vital tool that is important for modelling many complex numerical models. A model or system may be such that, for certain areas of input space, the output either does not exist, or is not in a quantifiable form. Here, we present a new method for classification where the model outputs are given distinct classifying labels, which we model using a latent Gaussian process (GP). The latent variable is estimated using MCMC sampling, a unique likelihood and distinct prior specifications. Our classifier is then verified by calculating a misclassification rate across the input space. Comparisons are made with other existing classification methods including logistic regression, which models the probability of being classified into one of two regions. To make classification predictions we draw from an independent Bernoulli distribution, meaning that distance correlation is lost from the independent draws and so can result in many misclassifications. By modelling the labels using a latent GP, this problem does not occur in our method. We apply our novel method to a range of examples including a motivating example which models the hormones associated with the reproductive system in mammals, where the two labelled outputs are high and low rates of reproduction.