Optimal threshold estimation for binary classifiers using game theory
- Autores
- Sánchez Miguel, Ignacio Enrique
- Año de publicación
- 2016
- Idioma
- inglés
- Tipo de recurso
- artículo
- Estado
- versión publicada
- Descripción
- Many bioinformatics algorithms can be understood as binary classifiers. They are usually compared using the area under the receiver operating characteristic (ROC) curve. On the other hand, choosing the best threshold for practical use is a complex task, due to uncertain and context-dependent skews in the abundance of positives in nature and in the yields/costs for correct/incorrect classification. We argue that considering a classifier as a player in a zero-sum game allows us to use the minimax principle from game theory to determine the optimal operating point. The proposed classifier threshold corresponds to the intersection between the ROC curve and the descending diagonal in ROC space and yields a minimax accuracy of 1-FPR. Our proposal can be readily implemented in practice, and reveals that the empirical condition for threshold estimation of “specificity equals sensitivity” maximizes robustness against uncertainties in the abundance of positives in nature and classification costs.
Fil: Sánchez Miguel, Ignacio Enrique. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Química Biológica de la Facultad de Ciencias Exactas y Naturales. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Química Biológica de la Facultad de Ciencias Exactas y Naturales; Argentina - Materia
-
GAME THEORY
ROC CURVE
MINIMAX PRINCIPLE
BIONFORMATICS - Nivel de accesibilidad
- acceso abierto
- Condiciones de uso
- https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
- Repositorio
- Institución
- Consejo Nacional de Investigaciones Científicas y Técnicas
- OAI Identificador
- oai:ri.conicet.gov.ar:11336/60009
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Optimal threshold estimation for binary classifiers using game theorySánchez Miguel, Ignacio EnriqueGAME THEORYROC CURVEMINIMAX PRINCIPLEBIONFORMATICShttps://purl.org/becyt/ford/1.6https://purl.org/becyt/ford/1Many bioinformatics algorithms can be understood as binary classifiers. They are usually compared using the area under the receiver operating characteristic (ROC) curve. On the other hand, choosing the best threshold for practical use is a complex task, due to uncertain and context-dependent skews in the abundance of positives in nature and in the yields/costs for correct/incorrect classification. We argue that considering a classifier as a player in a zero-sum game allows us to use the minimax principle from game theory to determine the optimal operating point. The proposed classifier threshold corresponds to the intersection between the ROC curve and the descending diagonal in ROC space and yields a minimax accuracy of 1-FPR. Our proposal can be readily implemented in practice, and reveals that the empirical condition for threshold estimation of “specificity equals sensitivity” maximizes robustness against uncertainties in the abundance of positives in nature and classification costs.Fil: Sánchez Miguel, Ignacio Enrique. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Química Biológica de la Facultad de Ciencias Exactas y Naturales. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Química Biológica de la Facultad de Ciencias Exactas y Naturales; ArgentinaF1000 Research Ltd2016-11-25info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/60009Sánchez Miguel, Ignacio Enrique; Optimal threshold estimation for binary classifiers using game theory; F1000 Research Ltd; F1000Research; 5; 2762; 25-11-2016; 1-122046-1402CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/https://f1000research.com/articles/5-2762/v3info:eu-repo/semantics/altIdentifier/doi/10.12688/f1000research.10114.3info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-29T10:08:31Zoai:ri.conicet.gov.ar:11336/60009instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-09-29 10:08:32.263CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse |
dc.title.none.fl_str_mv |
Optimal threshold estimation for binary classifiers using game theory |
title |
Optimal threshold estimation for binary classifiers using game theory |
spellingShingle |
Optimal threshold estimation for binary classifiers using game theory Sánchez Miguel, Ignacio Enrique GAME THEORY ROC CURVE MINIMAX PRINCIPLE BIONFORMATICS |
title_short |
Optimal threshold estimation for binary classifiers using game theory |
title_full |
Optimal threshold estimation for binary classifiers using game theory |
title_fullStr |
Optimal threshold estimation for binary classifiers using game theory |
title_full_unstemmed |
Optimal threshold estimation for binary classifiers using game theory |
title_sort |
Optimal threshold estimation for binary classifiers using game theory |
dc.creator.none.fl_str_mv |
Sánchez Miguel, Ignacio Enrique |
author |
Sánchez Miguel, Ignacio Enrique |
author_facet |
Sánchez Miguel, Ignacio Enrique |
author_role |
author |
dc.subject.none.fl_str_mv |
GAME THEORY ROC CURVE MINIMAX PRINCIPLE BIONFORMATICS |
topic |
GAME THEORY ROC CURVE MINIMAX PRINCIPLE BIONFORMATICS |
purl_subject.fl_str_mv |
https://purl.org/becyt/ford/1.6 https://purl.org/becyt/ford/1 |
dc.description.none.fl_txt_mv |
Many bioinformatics algorithms can be understood as binary classifiers. They are usually compared using the area under the receiver operating characteristic (ROC) curve. On the other hand, choosing the best threshold for practical use is a complex task, due to uncertain and context-dependent skews in the abundance of positives in nature and in the yields/costs for correct/incorrect classification. We argue that considering a classifier as a player in a zero-sum game allows us to use the minimax principle from game theory to determine the optimal operating point. The proposed classifier threshold corresponds to the intersection between the ROC curve and the descending diagonal in ROC space and yields a minimax accuracy of 1-FPR. Our proposal can be readily implemented in practice, and reveals that the empirical condition for threshold estimation of “specificity equals sensitivity” maximizes robustness against uncertainties in the abundance of positives in nature and classification costs. Fil: Sánchez Miguel, Ignacio Enrique. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Química Biológica de la Facultad de Ciencias Exactas y Naturales. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Química Biológica de la Facultad de Ciencias Exactas y Naturales; Argentina |
description |
Many bioinformatics algorithms can be understood as binary classifiers. They are usually compared using the area under the receiver operating characteristic (ROC) curve. On the other hand, choosing the best threshold for practical use is a complex task, due to uncertain and context-dependent skews in the abundance of positives in nature and in the yields/costs for correct/incorrect classification. We argue that considering a classifier as a player in a zero-sum game allows us to use the minimax principle from game theory to determine the optimal operating point. The proposed classifier threshold corresponds to the intersection between the ROC curve and the descending diagonal in ROC space and yields a minimax accuracy of 1-FPR. Our proposal can be readily implemented in practice, and reveals that the empirical condition for threshold estimation of “specificity equals sensitivity” maximizes robustness against uncertainties in the abundance of positives in nature and classification costs. |
publishDate |
2016 |
dc.date.none.fl_str_mv |
2016-11-25 |
dc.type.none.fl_str_mv |
info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion http://purl.org/coar/resource_type/c_6501 info:ar-repo/semantics/articulo |
format |
article |
status_str |
publishedVersion |
dc.identifier.none.fl_str_mv |
http://hdl.handle.net/11336/60009 Sánchez Miguel, Ignacio Enrique; Optimal threshold estimation for binary classifiers using game theory; F1000 Research Ltd; F1000Research; 5; 2762; 25-11-2016; 1-12 2046-1402 CONICET Digital CONICET |
url |
http://hdl.handle.net/11336/60009 |
identifier_str_mv |
Sánchez Miguel, Ignacio Enrique; Optimal threshold estimation for binary classifiers using game theory; F1000 Research Ltd; F1000Research; 5; 2762; 25-11-2016; 1-12 2046-1402 CONICET Digital CONICET |
dc.language.none.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
info:eu-repo/semantics/altIdentifier/url/https://f1000research.com/articles/5-2762/v3 info:eu-repo/semantics/altIdentifier/doi/10.12688/f1000research.10114.3 |
dc.rights.none.fl_str_mv |
info:eu-repo/semantics/openAccess https://creativecommons.org/licenses/by-nc-sa/2.5/ar/ |
eu_rights_str_mv |
openAccess |
rights_invalid_str_mv |
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/ |
dc.format.none.fl_str_mv |
application/pdf application/pdf application/pdf |
dc.publisher.none.fl_str_mv |
F1000 Research Ltd |
publisher.none.fl_str_mv |
F1000 Research Ltd |
dc.source.none.fl_str_mv |
reponame:CONICET Digital (CONICET) instname:Consejo Nacional de Investigaciones Científicas y Técnicas |
reponame_str |
CONICET Digital (CONICET) |
collection |
CONICET Digital (CONICET) |
instname_str |
Consejo Nacional de Investigaciones Científicas y Técnicas |
repository.name.fl_str_mv |
CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas |
repository.mail.fl_str_mv |
dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar |
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1844613954118090752 |
score |
13.070432 |