The Economics of Classification: Error vs. Complexity

D de Ridder, EM Pekalska, RPW Duin

    Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

    7 Citations (Scopus)


    Although usually classifier error is the main concern in publications, in real applications classifier evaluation complexity may play a large role as well. In this paper, a simple economic model is proposed with which a trade-off between classifier error and calculated evaluation complexity can be formulated. This trade-off can then be used to judge the necessity of increasing sample size or number of features to decrease classification error or, conversely, feature extraction or prototype selection to decrease evaluation complexity. The model is applied to the benchmark problem of handwritten digit recognition and is shown to lead to interesting conclusions, given certain assumptions.
    Original languageUndefined/Unknown
    Title of host publicationICPR16, Proceedings
    EditorsR Kasturi, D Laurendeau, C Suen
    Place of PublicationLos Alamitos, CA
    Number of pages4
    ISBN (Print)0-7695-1696-3
    Publication statusPublished - 2002
    Event16th International Conference on Pattern Recognition (Quebec City, Canada), vol. II - Los Alamitos, CA
    Duration: 11 Aug 200215 Aug 2002

    Publication series

    PublisherIEEE Computer Society Press
    NameInternational Conference on Pattern Recognition
    ISSN (Print)1051-4651


    Conference16th International Conference on Pattern Recognition (Quebec City, Canada), vol. II


    • conference contrib. refereed
    • Conf.proc. > 3 pag

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