Combining One-Class Classifiers.

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

    128 Citations (Scopus)

    Abstract

    In the problem of one-class classification target objects should be distinguished from outlier objects. In this problem it is assumed that only information of the target class is available while nothing is known about the outlier class. Like standard two-class classifiers, one-class classifiers hardly ever fit the data distribution perfectly. Using only the best classifier and discarding the classifiers with poorer performance might waste valuable information. To improve performance theresults of different classifiers (which may differ in complexity or training algorithm) can be combined. This can not only increase the performance but it can also increase the robustness of the classification. Because for one-class classifiers only information of one of the classes is present, combining one-class classifiers is more difficult. In this paper we investigate if and how one-class classifiers can be combined best in a handwritten digit recognition problem.
    Original languageUndefined/Unknown
    Title of host publicationMultiple Classifier Systems, Proceedings Second International Workshop MCS 2001.
    EditorsJ Kittler, F Roli
    Place of PublicationBerlin
    PublisherSpringer
    Pages299-308
    Number of pages10
    ISBN (Print)3-540-42284-6
    Publication statusPublished - 2001

    Publication series

    Name
    PublisherSpringer Verlag
    NameLecture Notes in Computer Science
    Volume2096
    ISSN (Print)0302-9743

    Keywords

    • ZX Int.klas.verslagjaar < 2002

    Cite this