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On Model Evaluation Under Non-constant Class Imbalance

The result's identifiers

  • Result code in IS VaVaI

    <a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F20%3A00342402" target="_blank" >RIV/68407700:21230/20:00342402 - isvavai.cz</a>

  • Result on the web

    <a href="https://doi.org/10.1007/978-3-030-50423-6_6" target="_blank" >https://doi.org/10.1007/978-3-030-50423-6_6</a>

  • DOI - Digital Object Identifier

    <a href="http://dx.doi.org/10.1007/978-3-030-50423-6_6" target="_blank" >10.1007/978-3-030-50423-6_6</a>

Alternative languages

  • Result language

    angličtina

  • Original language name

    On Model Evaluation Under Non-constant Class Imbalance

  • Original language description

    Many real-world classification problems are significantly class-imbalanced to detriment of the class of interest. The standard set of proper evaluation metrics is well-known but the usual assumption is that the test dataset imbalance equals the real-world imbalance. In practice, this assumption is often broken for various reasons. The reported results are then often too optimistic and may lead to wrong conclusions about industrial impact and suitability of proposed techniques. We introduce methods (Supplementary code related to techniques described in this paper is available at: https://github.com/CiscoCTA/nci_eval) focusing on evaluation under non-constant class imbalance. We show that not only the absolute values of commonly used metrics, but even the order of classifiers in relation to the evaluation metric used is affected by the change of the imbalance rate. Finally, we demonstrate that using subsampling in order to get a test dataset with class imbalance equal to the one observed in the wild is not necessary, and eventually can lead to significant errors in classifier’s performance estimate.

  • Czech name

  • Czech description

Classification

  • Type

    D - Article in proceedings

  • CEP classification

  • OECD FORD branch

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

Result continuities

  • Project

    <a href="/en/project/EF16_019%2F0000765" target="_blank" >EF16_019/0000765: Research Center for Informatics</a><br>

  • Continuities

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

Others

  • Publication year

    2020

  • Confidentiality

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

Data specific for result type

  • Article name in the collection

    Computational Science - ICCS 2020

  • ISBN

    978-3-030-50422-9

  • ISSN

    0302-9743

  • e-ISSN

    1611-3349

  • Number of pages

    14

  • Pages from-to

    74-87

  • Publisher name

    Springer

  • Place of publication

    Cham

  • Event location

    Amsterdam

  • Event date

    Jun 3, 2020

  • Type of event by nationality

    WRD - Celosvětová akce

  • UT code for WoS article