Comparing Performance of Different Inductive and Transductive Conformal Predictors Relevant to Drug Discovery

Lars Carlsson, Claus Bendtsen, Ernst Ahlberg
Proceedings of the Sixth Workshop on Conformal and Probabilistic Prediction and Applications, PMLR 60:201-212, 2017.

Abstract

We present an evaluation of the impact of transductive, inductive, aggregated and cross inductive mondrian conformal prediction on the validity and efficiency of predictions. The aim of the study is to give guidance to which methods perform best where there is limited data. The evaluation has been made on a large public dataset of Ames mutagenicity data, relevant for drug discovery, a spam dataset and a diverse set of drug discovery datasets. When considering predictions only, the transductive conformal predictor performs the best in terms of validity. If however more information is required, for example interpretation of a prediction, then any of the methods that calculate an averaged p-value should be considered.

Cite this Paper

BibTeX
@InProceedings{pmlr-v60-carlsson17a, title = {Comparing Performance of Different Inductive and Transductive Conformal Predictors Relevant to Drug Discovery}, author = {Carlsson, Lars and Bendtsen, Claus and Ahlberg, Ernst}, booktitle = {Proceedings of the Sixth Workshop on Conformal and Probabilistic Prediction and Applications}, pages = {201--212}, year = {2017}, editor = {Gammerman, Alex and Vovk, Vladimir and Luo, Zhiyuan and Papadopoulos, Harris}, volume = {60}, series = {Proceedings of Machine Learning Research}, month = {13--16 Jun}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v60/carlsson17a/carlsson17a.pdf}, url = {https://proceedings.mlr.press/v60/carlsson17a.html}, abstract = {We present an evaluation of the impact of transductive, inductive, aggregated and cross inductive mondrian conformal prediction on the validity and efficiency of predictions. The aim of the study is to give guidance to which methods perform best where there is limited data. The evaluation has been made on a large public dataset of Ames mutagenicity data, relevant for drug discovery, a spam dataset and a diverse set of drug discovery datasets. When considering predictions only, the transductive conformal predictor performs the best in terms of validity. If however more information is required, for example interpretation of a prediction, then any of the methods that calculate an averaged p-value should be considered.} }
Endnote
%0 Conference Paper %T Comparing Performance of Different Inductive and Transductive Conformal Predictors Relevant to Drug Discovery %A Lars Carlsson %A Claus Bendtsen %A Ernst Ahlberg %B Proceedings of the Sixth Workshop on Conformal and Probabilistic Prediction and Applications %C Proceedings of Machine Learning Research %D 2017 %E Alex Gammerman %E Vladimir Vovk %E Zhiyuan Luo %E Harris Papadopoulos %F pmlr-v60-carlsson17a %I PMLR %P 201--212 %U https://proceedings.mlr.press/v60/carlsson17a.html %V 60 %X We present an evaluation of the impact of transductive, inductive, aggregated and cross inductive mondrian conformal prediction on the validity and efficiency of predictions. The aim of the study is to give guidance to which methods perform best where there is limited data. The evaluation has been made on a large public dataset of Ames mutagenicity data, relevant for drug discovery, a spam dataset and a diverse set of drug discovery datasets. When considering predictions only, the transductive conformal predictor performs the best in terms of validity. If however more information is required, for example interpretation of a prediction, then any of the methods that calculate an averaged p-value should be considered.
APA
Carlsson, L., Bendtsen, C. & Ahlberg, E.. (2017). Comparing Performance of Different Inductive and Transductive Conformal Predictors Relevant to Drug Discovery. Proceedings of the Sixth Workshop on Conformal and Probabilistic Prediction and Applications, in Proceedings of Machine Learning Research 60:201-212 Available from https://proceedings.mlr.press/v60/carlsson17a.html.

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