0551866 20230418204528.920220117235959.9 85121331364 000728657100001 10.1080/10556788.2021.1965601 32 s. P cav_un_epca*02545881055-6788375 (2022)1636-1667Taylor & Francis general framework classification ranking accuracy at the top Neyman–Pearson Pat&Mat cav_un_auth*0313213 Adam L. CZ cav_un_auth*0422257 Mácha V. CZ cav_un_auth*0101207 Šmídl Václav UTIA-B Adaptivní systémy Department of Adaptive Systems AS AS Department of Adaptive Systems Ústav teorie informace a automatizace AV ČR, v. v. i. cav_un_auth*0307300 Pevný T. CZ http://library.utia.cas.cz/separaty/2022/AS/smidl-0551866.pdf https://www.tandfonline.com/doi/full/10.1080/10556788.2021.1965601 GA18-21409S GA ČR cav_un_auth*0374053 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. WOS BC 10000 10100 10102 2023 4 RVO:67985556 https://hdl.handle.net/11104/0337818 S 3+4 Article Computer Science Software Engineering|Operations Research Management Science|Mathematics Applied A OPERATIONSRESEARCH&MANAGEMENTSCIENCE|COMPUTERSCIENCE.SOFTWAREENGINEERING|MATHEMATICS.APPLIED 2.1 0.4 10.7 0.00319 1.039 2342 1.079 2.200 49 Q1 57.4 Q1 Q2 0.69 Q2 81.8 2022 85121331364 SCOPUS PUBMED 000728657100001 WOS cav_un_epca*0254588 Optimization Methods & Software 1055-6788 1029-4937 Roč. 37 č. 5 2022 1636 1667 Taylor & Francis