Logo des Repositoriums
 

FEERCI: A Package for Fast Non-Parametric Confidence Intervals for Equal Error Rates in Amortized O(m log n)

Kurzanzeige
dc.contributor.authorHaasnoot, Erwin
dc.contributor.authorKhodabakhsh, Ali
dc.contributor.authorZeinstra, Chris
dc.contributor.authorSpreeuwers, Luuk
dc.contributor.authorVeldhuis, Raymond
dc.contributor.editorBrömme, Arslan
dc.contributor.editorBusch, Christoph
dc.contributor.editorDantcheva, Antitza
dc.contributor.editorRathgeb, Christian
dc.contributor.editorUhl, Andreas
dc.date.accessioned2019-06-17T10:00:27Z
dc.date.available2019-06-17T10:00:27Z
dc.date.issued2018
dc.description.abstractEqual Error Rates (EERs), or other weighted relations between False Match and Non- Match Rates (FMR/FNMR), are often used as a performance metric for biometric systems. Confidence Intervals (CIs) are used to denote the uncertainty underlying these EERs, with many methods existing to estimate said CIs in both parametric and non-parametric ways. These confidence intervals provide, foremost, a method of comparing scoring/ranking functions. Non-parametric methods often suffer from high computational costs, but do not make assumptions as to the shape of the EERand score distributions. For both EERs and CIs, contemporary open-source toolkits leave room for improvement in terms of computational efficiency. In this paper, we introduce the Fast EER (FEER) algorithm that calculates an EER in O(logn) on a sorted score list, we show how to adapt the FEER algorithm to calculate non-parametric, bootstrapped EER CIs (FEERCI) in O(mlogn) given m resamplings, and we introduce an opinionated open-source package named feerci that provides implementations of the FEER and FEERCI algorithm.We provide speed and accuracy benchmarks for the feerci package, comparing it against the most-used methods of calculating EERs in Python and show how it is able to calculate EERs and CIs on very large score lists faster than contemporary toolkits can calculate a single EER.en
dc.identifier.isbn978-3-88579-676-4
dc.identifier.pissn1617-5468
dc.identifier.urihttps://dl.gi.de/handle/20.500.12116/23806
dc.language.isoen
dc.publisherKöllen Druck+Verlag GmbH
dc.relation.ispartofBIOSIG 2018 - Proceedings of the 17th International Conference of the Biometrics Special Interest Group
dc.relation.ispartofseriesLecture Notes in Informatics (LNI) - Proceedings, Volume P-283
dc.subjectReceiver operating characteristic
dc.subjectEqual Error Rate
dc.subjectBootstrap Confidence Interval
dc.subjectOpen Source.
dc.titleFEERCI: A Package for Fast Non-Parametric Confidence Intervals for Equal Error Rates in Amortized O(m log n)en
dc.typeText/Conference Paper
gi.citation.publisherPlaceBonn
gi.conference.date26.-28. September 2018
gi.conference.locationDarmstadt

Dateien

Originalbündel
1 - 1 von 1
Vorschaubild
Name:
update_BIOSIG_2018_paper_25.pdf
Größe:
149.5 KB
Format:
Adobe Portable Document Format
Herunterladen

Sammlungen

P282 - BIOSIG 2018 - Proceedings of the 17th International Conference of the Biometrics Special Interest Group