Combination of Conformal Predictors for Classification

Paolo Toccaceli, Alexander Gammerman

Research output: Chapter in Book/Report/Conference proceedingConference contribution


The paper presents some possible approaches to the combination of Conformal Predictors in the binary classification case. A first class of methods is based on p-value combination techniques that have been proposed in the context of Statistical Hypothesis Testing; a second class is based on the calibration of p-values into Bayes factors. A few methods from these two classes are applied to a real-world case, namely the chemoinformatics problem of Compound Activity Prediction. Their performance is discussed, showing the different abilities to preserve of validity and improve efficiency. The experiments show that P-value combination, in particular Fisher’s method, can be advantageous when ranking compounds by strength of evidence.
Original languageEnglish
Title of host publicationProceedings of Machine Learning Research
EditorsNeil Lawrence, Mark Reid
Number of pages23
Publication statusPublished - 13 Jun 2017

Publication series

NameThe Proceedings of Machine Learning Research
ISSN (Electronic)1938-7228

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