Making Likelihood Ratios Digestible for Cross-Application Performance Assessment

Andreas Nautsch, Didier Meuwly, Daniel Ramos, Jonas Lindh, Christoph Busch

    Research output: Contribution to journalArticleAcademicpeer-review

    3 Citations (Scopus)
    34 Downloads (Pure)

    Abstract

    Performance estimation is crucial to the assessment of novel algorithms and systems. In detection error tradeoff (DET) diagrams, discrimination performance is solely assessed targeting one application, where cross-application performance considers risks resulting from decisions, depending on application constraints. For the purpose of interchangeability of research results across different application constraints, we propose to augment DET curves by depicting systems regarding their support of security and convenience levels. Therefore, application policies are aggregated into levels based on verbal likelihood ratio scales, providing an easy to use concept for business-to-business communication to denote operative thresholds. We supply a reference implementation in Python, an exemplary performance assessment on synthetic score distributions, and a fine-tuning scheme for Bayes decision thresholds, when decision policies are bounded rather than fix
    Original languageEnglish
    Article number17176768
    Pages (from-to)1552-1556
    Number of pages5
    JournalIEEE signal processing letters
    Volume24
    Issue number10
    DOIs
    Publication statusPublished - 4 Sep 2017

    Keywords

    • Bayes decision framework
    • biometric verification
    • binary decisions
    • detection error tradeoff (DET)
    • verbal scales

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