Increasing the sensitivity of cancer predictors using confidence based ensemble modeling

Michael Affenzeller; Karin Zölzer; Stephan M. Winkler; Erwin Hopf; Herbert Stekel; Rupert Frechinger; Stefan Wagner

doi:10.1007/978-3-319-27340-2_44

Increasing the sensitivity of cancer predictors using confidence based ensemble modeling

Michael Affenzeller, Karin Zölzer, Stephan M. Winkler, Erwin Hopf, Herbert Stekel, Rupert Frechinger, Stefan Wagner

Research Center Hagenberg

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

1 Citation (Scopus)

Abstract

This paper discusses the use of symbolic regression based ensemble modeling for obtaining more sensitive cancer predictors. The ensemble models are generated on the basis of blood parameters acting as model inputs which have been coupled with diagnosis data in order to predict breast cancer. In addition to previous works this contribution focuses on the use of ensemble predictors in order to achieve more sensitive models. For achieving this goal the best models in terms of accuracy, sensitivity and in terms of a combined measure are selected based on training data in order to analyze to which extent the more sensitive model behavior is also reflected on test data. In addition to the a-posteriori selection of ensemble models with certain properties first results are shown that have been achieved with a new evaluation function which favors more sensitive predictors and guides the search towards more sensitive models already in the model generation phase.

Original language	English
Title of host publication	Computer Aided Systems Theory – EUROCAST 2015 - 15th International Conference, Revised Selected Papers
Editors	Franz Pichler, Roberto Moreno-Díaz, Alexis Quesada-Arencibia
Publisher	Springer
Pages	350-358
Number of pages	9
ISBN (Print)	9783319273396
DOIs	https://doi.org/10.1007/978-3-319-27340-2_44
Publication status	Published - 2015
Event	15th International Conference on Computer Aided Systems Theory, Eurocast 2015 - Las Palmas, Gran Canaria, Spain Duration: 8 Feb 2015 → 13 Feb 2015 http://eurocast2015.fulp.ulpgc.es/

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	9520
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	15th International Conference on Computer Aided Systems Theory, Eurocast 2015
Country/Territory	Spain
City	Las Palmas, Gran Canaria
Period	08.02.2015 → 13.02.2015
Internet address	http://eurocast2015.fulp.ulpgc.es/

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1007/978-3-319-27340-2_44

Cite this

Affenzeller, M., Zölzer, K., Winkler, S. M., Hopf, E., Stekel, H., Frechinger, R., & Wagner, S. (2015). Increasing the sensitivity of cancer predictors using confidence based ensemble modeling. In F. Pichler, R. Moreno-Díaz, & A. Quesada-Arencibia (Eds.), Computer Aided Systems Theory – EUROCAST 2015 - 15th International Conference, Revised Selected Papers (pp. 350-358). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9520). Springer. https://doi.org/10.1007/978-3-319-27340-2_44

Affenzeller, Michael ; Zölzer, Karin ; Winkler, Stephan M. et al. / Increasing the sensitivity of cancer predictors using confidence based ensemble modeling. Computer Aided Systems Theory – EUROCAST 2015 - 15th International Conference, Revised Selected Papers. editor / Franz Pichler ; Roberto Moreno-Díaz ; Alexis Quesada-Arencibia. Springer, 2015. pp. 350-358 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{922b576a61c942ed9eee084e96192a44,

title = "Increasing the sensitivity of cancer predictors using confidence based ensemble modeling",

abstract = "This paper discusses the use of symbolic regression based ensemble modeling for obtaining more sensitive cancer predictors. The ensemble models are generated on the basis of blood parameters acting as model inputs which have been coupled with diagnosis data in order to predict breast cancer. In addition to previous works this contribution focuses on the use of ensemble predictors in order to achieve more sensitive models. For achieving this goal the best models in terms of accuracy, sensitivity and in terms of a combined measure are selected based on training data in order to analyze to which extent the more sensitive model behavior is also reflected on test data. In addition to the a-posteriori selection of ensemble models with certain properties first results are shown that have been achieved with a new evaluation function which favors more sensitive predictors and guides the search towards more sensitive models already in the model generation phase.",

author = "Michael Affenzeller and Karin Z{\"o}lzer and Winkler, {Stephan M.} and Erwin Hopf and Herbert Stekel and Rupert Frechinger and Stefan Wagner",

year = "2015",

doi = "10.1007/978-3-319-27340-2_44",

language = "English",

isbn = "9783319273396",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

publisher = "Springer",

pages = "350--358",

editor = "Franz Pichler and Roberto Moreno-D{\'i}az and Alexis Quesada-Arencibia",

booktitle = "Computer Aided Systems Theory – EUROCAST 2015 - 15th International Conference, Revised Selected Papers",

address = "Germany",

note = "15th International Conference on Computer Aided Systems Theory, Eurocast 2015 ; Conference date: 08-02-2015 Through 13-02-2015",

url = "http://eurocast2015.fulp.ulpgc.es/",

}

Affenzeller, M, Zölzer, K, Winkler, SM, Hopf, E, Stekel, H, Frechinger, R & Wagner, S 2015, Increasing the sensitivity of cancer predictors using confidence based ensemble modeling. in F Pichler, R Moreno-Díaz & A Quesada-Arencibia (eds), Computer Aided Systems Theory – EUROCAST 2015 - 15th International Conference, Revised Selected Papers. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 9520, Springer, pp. 350-358, 15th International Conference on Computer Aided Systems Theory, Eurocast 2015, Las Palmas, Gran Canaria, Spain, 08.02.2015. https://doi.org/10.1007/978-3-319-27340-2_44

Increasing the sensitivity of cancer predictors using confidence based ensemble modeling. / Affenzeller, Michael; Zölzer, Karin; Winkler, Stephan M. et al.
Computer Aided Systems Theory – EUROCAST 2015 - 15th International Conference, Revised Selected Papers. ed. / Franz Pichler; Roberto Moreno-Díaz; Alexis Quesada-Arencibia. Springer, 2015. p. 350-358 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9520).

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

TY - GEN

T1 - Increasing the sensitivity of cancer predictors using confidence based ensemble modeling

AU - Affenzeller, Michael

AU - Zölzer, Karin

AU - Winkler, Stephan M.

AU - Hopf, Erwin

AU - Stekel, Herbert

AU - Frechinger, Rupert

AU - Wagner, Stefan

PY - 2015

Y1 - 2015

N2 - This paper discusses the use of symbolic regression based ensemble modeling for obtaining more sensitive cancer predictors. The ensemble models are generated on the basis of blood parameters acting as model inputs which have been coupled with diagnosis data in order to predict breast cancer. In addition to previous works this contribution focuses on the use of ensemble predictors in order to achieve more sensitive models. For achieving this goal the best models in terms of accuracy, sensitivity and in terms of a combined measure are selected based on training data in order to analyze to which extent the more sensitive model behavior is also reflected on test data. In addition to the a-posteriori selection of ensemble models with certain properties first results are shown that have been achieved with a new evaluation function which favors more sensitive predictors and guides the search towards more sensitive models already in the model generation phase.

AB - This paper discusses the use of symbolic regression based ensemble modeling for obtaining more sensitive cancer predictors. The ensemble models are generated on the basis of blood parameters acting as model inputs which have been coupled with diagnosis data in order to predict breast cancer. In addition to previous works this contribution focuses on the use of ensemble predictors in order to achieve more sensitive models. For achieving this goal the best models in terms of accuracy, sensitivity and in terms of a combined measure are selected based on training data in order to analyze to which extent the more sensitive model behavior is also reflected on test data. In addition to the a-posteriori selection of ensemble models with certain properties first results are shown that have been achieved with a new evaluation function which favors more sensitive predictors and guides the search towards more sensitive models already in the model generation phase.

UR - http://www.scopus.com/inward/record.url?scp=84952331944&partnerID=8YFLogxK

U2 - 10.1007/978-3-319-27340-2_44

DO - 10.1007/978-3-319-27340-2_44

M3 - Conference contribution

AN - SCOPUS:84952331944

SN - 9783319273396

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 350

EP - 358

BT - Computer Aided Systems Theory – EUROCAST 2015 - 15th International Conference, Revised Selected Papers

A2 - Pichler, Franz

A2 - Moreno-Díaz, Roberto

A2 - Quesada-Arencibia, Alexis

PB - Springer

T2 - 15th International Conference on Computer Aided Systems Theory, Eurocast 2015

Y2 - 8 February 2015 through 13 February 2015

ER -

Affenzeller M, Zölzer K, Winkler SM, Hopf E, Stekel H, Frechinger R et al. Increasing the sensitivity of cancer predictors using confidence based ensemble modeling. In Pichler F, Moreno-Díaz R, Quesada-Arencibia A, editors, Computer Aided Systems Theory – EUROCAST 2015 - 15th International Conference, Revised Selected Papers. Springer. 2015. p. 350-358. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-319-27340-2_44

Increasing the sensitivity of cancer predictors using confidence based ensemble modeling

Abstract

Publication series

Conference

UN SDGs

Access to Document

Other files and links

Fingerprint

Cite this