Optimal Strategies for Reject Option Classifiers

Result code in 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>

Result on the web

<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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Result language

angličtina

Original language name

Optimal Strategies for Reject Option Classifiers

Original language description

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.

Czech name

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Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

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OECD FORD branch

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

Project

Result was created during the realization of more than one project. More information in the Projects tab.

Continuities

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

Publication year

2023

Confidentiality

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

Name of the periodical

Journal of Machine Learning Research

ISSN

1532-4435

e-ISSN

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Volume of the periodical

24

Issue of the periodical within the volume

Jan-Dec

Country of publishing house

US - UNITED STATES

Number of pages

49

Pages from-to

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UT code for WoS article

001111709400001

EID of the result in the Scopus database

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