Improving the accuracy of cancer prediction by ensemble confidence evaluation

Michael Affenzeller, Stephan Winkler, Herbert Stekel, Stefan Forstenlechner, Stefan Wagner

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

4 Citations (Scopus)

Abstract

This paper discusses a novel approach for the prediction of breast cancer, melanoma and cancer in the respiratory system using ensemble modeling techniques. For each type of cancer, a set of unequally complex predictors are learned by symbolic classification based on genetic programming. In addition to standard ensemble modeling, where the prediction is based on a majority voting of the prediction models, two confidence parameters are used which aim to quantify the trustworthiness of each single prediction based on the clearness of the majority voting. Based on the calculated confidence of each ensemble prediction, predictions might be considered uncertain. The experimental part of this paper discusses the increase of accuracy that can be obtained for those samples which are considered trustable depending on the ratio of predictions that are considered trustable.

Original languageEnglish
Title of host publicationComputer Aided Systems Theory, EUROCAST 2013 - 14th International Conference, Revised Selected Papers
PublisherSpringer
Pages316-323
Number of pages8
EditionPART 1
ISBN (Print)9783642538551
DOIs
Publication statusPublished - 2013
Event14th International Conference on Computer Aided Systems Theory, Eurocast 2013 - Las Palmas de Gran Canaria, Spain
Duration: 10 Feb 201315 Feb 2013

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 1
Volume8111 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference14th International Conference on Computer Aided Systems Theory, Eurocast 2013
CountrySpain
CityLas Palmas de Gran Canaria
Period10.02.201315.02.2013

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