2019/09/12 by Georgi Nalbantov, Nalbantov, Georgi, Svetoslav Ivanov +1 · 2 citations
Computer Science · Engineering · Mathematics · #Advanced Statistical Methods and Models #Anomaly Detection Techniques and Applications #FOS: Computer and information sciences #Fault Detection and Control Systems #Machine Learning (cs.LG) #Machine Learning (stat.ML)
paper · pdf · doi:10.48550/arxiv.1909.05894
openalex publication_date 2019/09/12 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
One of the central themes in the classification task is the estimation of class posterior probability at a new point \bfx. The vast majority of classifiers output a score for \bfx, which is monotonically related to the posterior probability via an unknown relationship. There are many attempts in the literature to estimate this latter relationship. Here, we provide a way to estimate the posterior probability without resorting to using classification scores. Instead, we vary the prior probabilities of classes in order to derive the ratio of pdf's at point \bfx, which is directly used to determine class posterior probabilities. We consider here the binary classification problem.