Combination of Conformal Predictors for Classification. / Toccaceli, Paolo; Gammerman, Alexander.

Proceedings of Machine Learning Research. ed. / Neil Lawrence; Mark Reid. Vol. 60 2017. p. 39-61 (The Proceedings of Machine Learning Research; Vol. 60).

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

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Abstract

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
Pages39-61
Number of pages23
Volume60
Publication statusPublished - 13 Jun 2017

Publication series

NameThe Proceedings of Machine Learning Research
Volume60
ISSN (Electronic)1938-7228
This open access research output is licenced under a Creative Commons Attribution-NonCommercial-NoDerivs 3.0 Unported License.

ID: 29014060