The Combining Classifier: To Train Or Not To Train?

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

    256 Citations (Scopus)

    Abstract

    When more than a single classifier has been trained for the same recognition problem the question arises how this set of classifiers may be combined into a final decision rule. Several fixed combining rules are used that depend on the output values of the base classifiers only. They are almost always suboptimal. Usually, however, training sets are available. They may be used to calibrate the base classifier outputs, as well as to build a trained combining classifier using these outputs as inputs. It depends on various circumstances whether this is useful, in particular whether the training set is used for the base classifiers as well and whether they are overtrained. We present an intuitive discussing on the use of trained combiners, relating the question of the choice of the combining classifier to a similar choice in the area of dissimilarity based pattern recognition. Some simple examples will be used to illustrate the discussion.
    Original languageUndefined/Unknown
    Title of host publicationICPR16, Proceedings
    EditorsR Kasturi, D Laurendeau, C Suen
    Place of PublicationLos Alamitos, CA
    PublisherIEEE
    Pages765-770
    Number of pages6
    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

    Name
    PublisherIEEE Computer Society Press
    NameInternational Conference on Pattern Recognition
    Volume2
    ISSN (Print)1051-4651

    Conference

    Conference16th International Conference on Pattern Recognition (Quebec City, Canada), vol. II
    Period11/08/0215/08/02

    Keywords

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

    Cite this