Binary classification trees for multi-class classification problems

    Research output: Contribution to conferenceConference paperpeer-review

    Abstract

    This paper proposes a binary classification tree aiming at solving multi-class classification problems using binary classifiers. The tree design is achieved in a way that a class group is partitioned into two distinct subgroups at a node. The node adopts the class-modular scheme to improve the binary classification capability. The partitioning is formulated as an optimization problem and a genetic algorithm is proposed to solve the optimization problem. The binary classification tree is compared to the conventional methods in terms of classification accuracy and timing efficiency. Experiments were performed with numeral recognition and touching-numeral pair recognition.

    Original languageEnglish
    Title of host publicationProceedings - 7th International Conference on Document Analysis and Recognition, ICDAR 2003
    PublisherIEEE Computer Society
    Pages770-774
    Number of pages5
    ISBN (Electronic)0769519601
    DOIs
    StatePublished - 2003
    Event7th International Conference on Document Analysis and Recognition, ICDAR 2003 - Edinburgh, United Kingdom
    Duration: 2003.08.32003.08.6

    Publication series

    NameProceedings of the International Conference on Document Analysis and Recognition, ICDAR
    Volume2003-January
    ISSN (Print)1520-5363

    Conference

    Conference7th International Conference on Document Analysis and Recognition, ICDAR 2003
    Country/TerritoryUnited Kingdom
    CityEdinburgh
    Period03.08.303.08.6

    Quacquarelli Symonds(QS) Subject Topics

    • Computer Science & Information Systems
    • Data Science

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