Optimal Strategies for Reject Option Classifiers

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F23%3A00365904" target="_blank" >RIV/68407700:21230/23:00365904 - isvavai.cz</a>

Výsledek na webu

<a href="https://jmlr.org/papers/v24/21-0048.html" target="_blank" >https://jmlr.org/papers/v24/21-0048.html</a>

DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

Optimal Strategies for Reject Option Classifiers

Popis výsledku v původním jazyce

In classification with a reject option, the classifier is allowed in uncertain cases to abstain from prediction. The classical cost-based model of a reject option classifier requires the rejection cost to be defined explicitly. The alternative bounded-improvement model and the bounded-abstention model avoid the notion of the reject cost. The bounded-improvement model seeks a classifier with a guaranteed selective risk and maximal cover. The bounded-abstention model seeks a classifier with guaranteed cover and minimal selective risk. We prove that despite their different formulations the three rejection models lead to the same prediction strategy: the Bayes classifier endowed with a randomized Bayes selection function. We define the notion of a proper uncertainty score as a scalar summary of the prediction uncertainty sufficient to construct the randomized Bayes selection function. We propose two algorithms to learn the proper uncertainty score from examples for an arbitrary black-box classifier. We prove that both algorithms provide Fisher consistent estimates of the proper uncertainty score and demonstrate their efficiency in different prediction problems, including classification, ordinal regression, and structured output classification.

Název v anglickém jazyce

Optimal Strategies for Reject Option Classifiers

Popis výsledku anglicky

In classification with a reject option, the classifier is allowed in uncertain cases to abstain from prediction. The classical cost-based model of a reject option classifier requires the rejection cost to be defined explicitly. The alternative bounded-improvement model and the bounded-abstention model avoid the notion of the reject cost. The bounded-improvement model seeks a classifier with a guaranteed selective risk and maximal cover. The bounded-abstention model seeks a classifier with guaranteed cover and minimal selective risk. We prove that despite their different formulations the three rejection models lead to the same prediction strategy: the Bayes classifier endowed with a randomized Bayes selection function. We define the notion of a proper uncertainty score as a scalar summary of the prediction uncertainty sufficient to construct the randomized Bayes selection function. We propose two algorithms to learn the proper uncertainty score from examples for an arbitrary black-box classifier. We prove that both algorithms provide Fisher consistent estimates of the proper uncertainty score and demonstrate their efficiency in different prediction problems, including classification, ordinal regression, and structured output classification.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

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OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

Výsledek vznikl pri realizaci vícero projektů. Více informací v záložce Projekty.

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2023

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Journal of Machine Learning Research

ISSN

1532-4435

e-ISSN

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Svazek periodika

24

Číslo periodika v rámci svazku

Jan-Dec

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

49

Strana od-do

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Kód UT WoS článku

001111709400001

EID výsledku v databázi Scopus

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