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, Volodya Vovk, Mike Waterfield, Rainer Cramer, John F. Timms, Ian Jacobs, Usha Menon, Alexander Gammerman

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

7 Citations (Scopus)

Abstract

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
Subtitle of host publicationAIAB, AIeIA, CISE, COPA, IIVC, ISQL, MHDW, and WADTMB, Proceedings
Pages224-233
Number of pages10
EditionPART 2
DOIs
Publication statusPublished - 17 Dec 2012
Event8th International Workshop on Artificial Intelligence Applications and Innovations, AIAI 2012: AIAB, AIeIA, CISE, COPA, IIVC, ISQL, MHDW, and WADTMB - Halkidiki, Greece
Duration: 27 Sep 201230 Sep 2012

Publication series

NameIFIP Advances in Information and Communication Technology
NumberPART 2
Volume382 AICT
ISSN (Print)1868-4238

Conference

Conference8th International Workshop on Artificial Intelligence Applications and Innovations, AIAI 2012: AIAB, AIeIA, CISE, COPA, IIVC, ISQL, MHDW, and WADTMB
CountryGreece
CityHalkidiki
Period27/09/1230/09/12

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