Confidence-Based Ensemble Modeling in Medical Data Mining

Lukas Kammerer; Michael Affenzeller

doi:10.1145/3205651.3205722

Confidence-Based Ensemble Modeling in Medical Data Mining

Lukas Kammerer, Michael Affenzeller

Research output: Chapter in Book/Report/Conference proceedings › Conference contribution › peer-review

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 language	English
Title of host publication	GECCO 2018 Companion - Proceedings of the 2018 Genetic and Evolutionary Computation Conference Companion
Publisher	ACM Press
Pages	163-164
Number of pages	2
ISBN (Electronic)	9781450357647
DOIs	https://doi.org/10.1145/3205651.3205722
Publication status	Published - 6 Jul 2018
Event	Genetic and Evolutionary Computation Conference (GECCO 2018) - Kyoto, Japan, Japan Duration: 15 Jul 2018 → 19 Jul 2018 http://gecco-2018.sigevo.org/

Publication series

Name	GECCO 2018 Companion - Proceedings of the 2018 Genetic and Evolutionary Computation Conference Companion

Conference

Conference	Genetic and Evolutionary Computation Conference (GECCO 2018)
Country/Territory	Japan
City	Kyoto, Japan
Period	15.07.2018 → 19.07.2018
Internet address	http://gecco-2018.sigevo.org/

Keywords

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

Access to Document

10.1145/3205651.3205722

Cite this

Kammerer, L., & Affenzeller, M. (2018). Confidence-Based Ensemble Modeling in Medical Data Mining. In GECCO 2018 Companion - Proceedings of the 2018 Genetic and Evolutionary Computation Conference Companion (pp. 163-164). (GECCO 2018 Companion - Proceedings of the 2018 Genetic and Evolutionary Computation Conference Companion). ACM Press. https://doi.org/10.1145/3205651.3205722

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title = "Confidence-Based Ensemble Modeling in Medical Data Mining",

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.",

keywords = "Confidence-based ensemble modeling, Genetic programming, Machine learning, Medical data mining",

author = "Lukas Kammerer and Michael Affenzeller",

note = "Publisher Copyright: {\textcopyright} 2018 Copyright held by the owner/author(s). Copyright: Copyright 2018 Elsevier B.V., All rights reserved.; Genetic and Evolutionary Computation Conference (GECCO 2018) ; Conference date: 15-07-2018 Through 19-07-2018",

year = "2018",

month = jul,

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doi = "10.1145/3205651.3205722",

language = "English",

series = "GECCO 2018 Companion - Proceedings of the 2018 Genetic and Evolutionary Computation Conference Companion",

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Kammerer, L & Affenzeller, M 2018, Confidence-Based Ensemble Modeling in Medical Data Mining. in GECCO 2018 Companion - Proceedings of the 2018 Genetic and Evolutionary Computation Conference Companion. GECCO 2018 Companion - Proceedings of the 2018 Genetic and Evolutionary Computation Conference Companion, ACM Press, pp. 163-164, Genetic and Evolutionary Computation Conference (GECCO 2018), Kyoto, Japan, Japan, 15.07.2018. https://doi.org/10.1145/3205651.3205722

Confidence-Based Ensemble Modeling in Medical Data Mining. / Kammerer, Lukas ; Affenzeller, Michael.
GECCO 2018 Companion - Proceedings of the 2018 Genetic and Evolutionary Computation Conference Companion. ACM Press, 2018. p. 163-164 (GECCO 2018 Companion - Proceedings of the 2018 Genetic and Evolutionary Computation Conference Companion).

Research output: Chapter in Book/Report/Conference proceedings › Conference contribution › peer-review

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ER -

Confidence-Based Ensemble Modeling in Medical Data Mining

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Keywords

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