Multiprobabilistic Venn Predictors with Logistic Regression

Ilia Nouretdinov, Dmitry Devetyarov, Brian Burford, Stephane Camuzeaux, Aleksandra Gentry-Maharaj, Ali Tiss, Celia Smith, Zhiyuan Luo, Alexey Chervonenkis, Rachel Hallett, Vladimir Vovk, Mike Waterfield, Rainer Cramer, John F. Timms, Ian Jacobs, Usha Menon, Alex Gammerman

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


This paper describes the methodology of providing multiprobability predictions for proteomic mass spectrometry data. The methodology is based on a newly developed machine learning framework called Venn machines. They allow us to output a valid probability interval. We apply this methodology to mass spectrometry data sets in order to predict the diagnosis of heart disease and early diagnoses of ovarian cancer. The experiments show that probability intervals are valid and narrow. In addition, probability intervals were compared with the output of a corresponding probability predictor.
Original languageEnglish
Title of host publicationArtificial Intelligence Applications and Innovations - AIAI 2012 International Workshops: AIAB, AIeIA, CISE, COPA, IIVC, ISQL, MHDW, and WADTMB, Halkidiki, Greece, September 27-30, 2012, Proceedings, Part II
EditorsLazaros Iliadis, Ilias Maglogiannis, Harris Papadopoulos, Kostas Karatzas, Spyros Sioutas
ISBN (Electronic)978-3-642-33411-5
Publication statusPublished - 2012

Publication series

NameIFIP Advances in Information and Communication Technology

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