General framework for binary classification on top samples
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985556%3A_____%2F22%3A00551866" target="_blank" >RIV/67985556:_____/22:00551866 - isvavai.cz</a>
Alternative codes found
RIV/68407700:21230/22:00354903 RIV/68407700:21340/22:00354903
Result on the web
<a href="https://www.tandfonline.com/doi/full/10.1080/10556788.2021.1965601" target="_blank" >https://www.tandfonline.com/doi/full/10.1080/10556788.2021.1965601</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1080/10556788.2021.1965601" target="_blank" >10.1080/10556788.2021.1965601</a>
Alternative languages
Result language
angličtina
Original language name
General framework for binary classification on top samples
Original language description
Many binary classification problems minimize misclassification above (or below) a threshold. We show that instances of ranking problems, accuracy at the top, or hypothesis testing may be written in this form. We propose a general framework to handle these classes of problems and show which formulations (both known and newly proposed) fall into this framework. We provide a theoretical analysis of this framework and mention selected possible pitfalls the formulations may encounter. We show the convergence of the stochastic gradient descent for selected formulations even though the gradient estimate is inherently biased. We suggest several numerical improvements, including the implicit derivative and stochastic gradient descent. We provide an extensive numerical study.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
—
OECD FORD branch
10102 - Applied mathematics
Result continuities
Project
Result was created during the realization of more than one project. More information in the Projects tab.
Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2022
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Optimization Methods & Software
ISSN
1055-6788
e-ISSN
1029-4937
Volume of the periodical
37
Issue of the periodical within the volume
5
Country of publishing house
GB - UNITED KINGDOM
Number of pages
32
Pages from-to
1636-1667
UT code for WoS article
000728657100001
EID of the result in the Scopus database
2-s2.0-85121331364