On feature selection with measurement cost and grouped features

P Paclik, RPW Duin, GMP van Kempen, R Kohlus

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

    16 Citations (Scopus)


    Feature selection is an important tool reducing necessary feature acquisition time in some applications. Standard methods, proposed in the literature, do not cope with the measurement cost issue. Including the measurement cost into the feature selection process is difficult when features are grouped together due to the implementation. If one feature from a group is requested, all others are available for zero additional measurement cost. In the paper, we investigate two approaches how to use the measurement cost and feature grouping in the selection process. We show, that employing grouping improves the performance significantly for low measurement costs. We discuss an application where limiting the computation time is a very important topic: the segmentation of backscatter images in product analysis.
    Original languageUndefined/Unknown
    Title of host publicationStructural, Syntactic, and Statistical Pattern Recognition, Proceedings
    EditorsT Caelli, A Amin, RPW Duin, M Kamel, D de Ridder
    Place of PublicationBerlin
    Number of pages9
    ISBN (Print)3-540-44011-9
    Publication statusPublished - 2002
    EventJoint IAPR International Workshops SSPR'02 and SPR'02 (Windsor, Canada) - Berlin
    Duration: 6 Aug 20029 Aug 2002

    Publication series

    PublisherSpringer Verlag
    NameLecture Notes in Computer Science
    ISSN (Print)0302-9743


    ConferenceJoint IAPR International Workshops SSPR'02 and SPR'02 (Windsor, Canada)

    Bibliographical note

    ISSN 0302-9743, phpub 28


    • conference contrib. refereed
    • ZX CWTS JFIS < 1.00

    Cite this