Conformal Prediction in Learning Under Privileged Information Paradigm with Applications in Drug Discovery

Autor:	Gauraha, Niharika, Carlsson, Lars, Spjuth, Ola
Rok vydání:	2018
Předmět:	Statistics - Machine Learning Computer Science - Learning Statistics - Applications
Druh dokumentu:	Working Paper
Popis:	This paper explores conformal prediction in the learning under privileged information (LUPI) paradigm. We use the SVM+ realization of LUPI in an inductive conformal predictor, and apply it to the MNIST benchmark dataset and three datasets in drug discovery. The results show that using privileged information produces valid models and improves efficiency compared to standard SVM, however the improvement varies between the tested datasets and is not substantial in the drug discovery applications. More importantly, using SVM+ in a conformal prediction framework enables valid prediction intervals at specified significance levels.
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/1803.11136 Zobrazit plný text záznamu View this record from Arxiv