Model selection with PLANN-CR-ARD

Corneliu T C Arsene, Paulo J. Lisboa, Elia Biganzoli

Research output: Chapter in Book/Report/Conference proceedingConference contribution

9 Citations (Scopus)

Abstract

This paper presents a new compensation mechanism to be used with a Partial Logistic Artificial Neural Network for Competing Risks with Automatic Relevance Determination (PLANN-CR-ARD) and tested comprehensibly on a real breast cancer dataset with excellent convergence properties and numerical stability for the non-linear model. The Model Selection is implemented for the PLANN-CR-ARD model, benefiting from a scaling of the prior error term which together with the data error term forms the total error function that is optimized. The PLANN-CR-ARD proves to be an excellent prognostic tool that can be used in regression analysis tasks such as the survival analysis of cancer datasets.

Original languageEnglish
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Pages210-219
Number of pages10
Volume6692 LNCS
EditionPART 2
DOIs
Publication statusPublished - 2011
Event11th International Work-Conference on on Artificial Neural Networks, IWANN 2011 - Torremolinos-Malaga, Spain
Duration: Jun 8 2011Jun 10 2011

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 2
Volume6692 LNCS
ISSN (Print)03029743
ISSN (Electronic)16113349

Other

Other11th International Work-Conference on on Artificial Neural Networks, IWANN 2011
CountrySpain
CityTorremolinos-Malaga
Period6/8/116/10/11

Fingerprint

Competing Risks
Model Selection
Logistics
Artificial Neural Network
Error term
Neural networks
Partial
Compensation (personnel)
Survival Analysis
Error function
Convergence of numerical methods
Numerical Stability
Breast Cancer
Regression Analysis
Regression analysis
Convergence Properties
Nonlinear Model
Cancer
Scaling
Relevance

Keywords

  • Artificial Neural Networks
  • Competing Risks
  • Convergence properties
  • Model Selection
  • PLANN-CR-ARD

ASJC Scopus subject areas

  • Computer Science(all)
  • Theoretical Computer Science

Cite this

Arsene, C. T. C., Lisboa, P. J., & Biganzoli, E. (2011). Model selection with PLANN-CR-ARD. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (PART 2 ed., Vol. 6692 LNCS, pp. 210-219). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 6692 LNCS, No. PART 2). https://doi.org/10.1007/978-3-642-21498-1_27

Model selection with PLANN-CR-ARD. / Arsene, Corneliu T C; Lisboa, Paulo J.; Biganzoli, Elia.

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 6692 LNCS PART 2. ed. 2011. p. 210-219 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 6692 LNCS, No. PART 2).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Arsene, CTC, Lisboa, PJ & Biganzoli, E 2011, Model selection with PLANN-CR-ARD. in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). PART 2 edn, vol. 6692 LNCS, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), no. PART 2, vol. 6692 LNCS, pp. 210-219, 11th International Work-Conference on on Artificial Neural Networks, IWANN 2011, Torremolinos-Malaga, Spain, 6/8/11. https://doi.org/10.1007/978-3-642-21498-1_27
Arsene CTC, Lisboa PJ, Biganzoli E. Model selection with PLANN-CR-ARD. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). PART 2 ed. Vol. 6692 LNCS. 2011. p. 210-219. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 2). https://doi.org/10.1007/978-3-642-21498-1_27
Arsene, Corneliu T C ; Lisboa, Paulo J. ; Biganzoli, Elia. / Model selection with PLANN-CR-ARD. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 6692 LNCS PART 2. ed. 2011. pp. 210-219 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 2).
@inproceedings{6011553d76fe48ab907c2ce9e91274ec,
title = "Model selection with PLANN-CR-ARD",
abstract = "This paper presents a new compensation mechanism to be used with a Partial Logistic Artificial Neural Network for Competing Risks with Automatic Relevance Determination (PLANN-CR-ARD) and tested comprehensibly on a real breast cancer dataset with excellent convergence properties and numerical stability for the non-linear model. The Model Selection is implemented for the PLANN-CR-ARD model, benefiting from a scaling of the prior error term which together with the data error term forms the total error function that is optimized. The PLANN-CR-ARD proves to be an excellent prognostic tool that can be used in regression analysis tasks such as the survival analysis of cancer datasets.",
keywords = "Artificial Neural Networks, Competing Risks, Convergence properties, Model Selection, PLANN-CR-ARD",
author = "Arsene, {Corneliu T C} and Lisboa, {Paulo J.} and Elia Biganzoli",
year = "2011",
doi = "10.1007/978-3-642-21498-1_27",
language = "English",
isbn = "9783642214974",
volume = "6692 LNCS",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
number = "PART 2",
pages = "210--219",
booktitle = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
edition = "PART 2",

}

TY - GEN

T1 - Model selection with PLANN-CR-ARD

AU - Arsene, Corneliu T C

AU - Lisboa, Paulo J.

AU - Biganzoli, Elia

PY - 2011

Y1 - 2011

N2 - This paper presents a new compensation mechanism to be used with a Partial Logistic Artificial Neural Network for Competing Risks with Automatic Relevance Determination (PLANN-CR-ARD) and tested comprehensibly on a real breast cancer dataset with excellent convergence properties and numerical stability for the non-linear model. The Model Selection is implemented for the PLANN-CR-ARD model, benefiting from a scaling of the prior error term which together with the data error term forms the total error function that is optimized. The PLANN-CR-ARD proves to be an excellent prognostic tool that can be used in regression analysis tasks such as the survival analysis of cancer datasets.

AB - This paper presents a new compensation mechanism to be used with a Partial Logistic Artificial Neural Network for Competing Risks with Automatic Relevance Determination (PLANN-CR-ARD) and tested comprehensibly on a real breast cancer dataset with excellent convergence properties and numerical stability for the non-linear model. The Model Selection is implemented for the PLANN-CR-ARD model, benefiting from a scaling of the prior error term which together with the data error term forms the total error function that is optimized. The PLANN-CR-ARD proves to be an excellent prognostic tool that can be used in regression analysis tasks such as the survival analysis of cancer datasets.

KW - Artificial Neural Networks

KW - Competing Risks

KW - Convergence properties

KW - Model Selection

KW - PLANN-CR-ARD

UR - http://www.scopus.com/inward/record.url?scp=79957936804&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=79957936804&partnerID=8YFLogxK

U2 - 10.1007/978-3-642-21498-1_27

DO - 10.1007/978-3-642-21498-1_27

M3 - Conference contribution

SN - 9783642214974

VL - 6692 LNCS

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

SP - 210

EP - 219

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

ER -