Confidence-Based Ensemble Modeling in Medical Data Mining

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1 Citation (Scopus)

Abstract

A recent approach for improving the accuracy of ensemble models is confidence-based modeling. Thereby, confidence measures, which indicate an ensemble prediction's reliability, are used for identifying unreliable predictions in order to improve a model's accuracy among reliable predictions. However, despite promising results in previous work, no comparable results for public benchmark data sets have been published yet. This paper applies confidence-based modeling with GP-based symbolic binary classification ensembles on a set of medical benchmark problems to make statements about the concept's general applicability. Moreover, extensions for multiclass classification problems are proposed.

Original languageEnglish
Title of host publicationGECCO 2018 Companion - Proceedings of the 2018 Genetic and Evolutionary Computation Conference Companion
PublisherACM Press
Pages163-164
Number of pages2
ISBN (Electronic)9781450357647
DOIs
Publication statusPublished - 6 Jul 2018
EventGenetic and Evolutionary Computation Conference (GECCO 2018) - Kyoto, Japan, Japan
Duration: 15 Jul 201819 Jul 2018
http://gecco-2018.sigevo.org/

Publication series

NameGECCO 2018 Companion - Proceedings of the 2018 Genetic and Evolutionary Computation Conference Companion

Conference

ConferenceGenetic and Evolutionary Computation Conference (GECCO 2018)
CountryJapan
CityKyoto, Japan
Period15.07.201819.07.2018
Internet address

Keywords

  • Confidence-based ensemble modeling
  • Genetic programming
  • Machine learning
  • Medical data mining

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