Conformal predictor combination using Neyman–Pearson Lemma. / Toccaceli, Paolo.

Proceedings of the Eighth Symposium on Conformal and Probabilistic Prediction and Applications. Vol. 105 Proceedings of Machine Learning Research, 2019. p. 66-88.

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Abstract

The problem of how to combine advantageously Conformal Predictors (CP) has attracted the interest of many researchers in recent years. The challenge is to retain validity, while improving efficiency. In this article a very generic method is proposed which takes advantage of a well-established result in Classical Statistical Hypothesis Testing, the Neyman–Pearson Lemma, to combine CP with maximum efficiency. The merits and the limits of the method are explored on synthetic data sets under different levels of correlation between NonConformity Measures (NCM). CP Combination via Neyman–Pearson Lemma generally outperforms other combination methods when an accurate and robust density ratio estimation method, such as the V-Matrix method, is used.
Original languageEnglish
Title of host publicationProceedings of the Eighth Symposium on Conformal and Probabilistic Prediction and Applications
PublisherProceedings of Machine Learning Research
Pages66-88
Number of pages23
Volume105
Publication statusPublished - 2019
This open access research output is licenced under a Creative Commons Attribution-NonCommercial-NoDerivs 3.0 Unported License.

ID: 39120389