Learn++.MT: A new approach to incremental learning

Michael Muhlbaier, Apostolos Topalis, Robi Polikar

    Research output: Chapter in Book/Report/Conference proceedingChapter

    30 Scopus citations

    Abstract

    An ensemble of classifiers based algorithm, Learn++, was recently introduced that is capable of incrementally learning new information from datasets that consecutively become available, even if the new data introduce additional classes that were not formerly seen. The algorithm does not require access to previously used datasets, yet it is capable of largely retaining the previously acquired knowledge. However, Learn++ suffers from the inherent "out-voting" problem when asked to learn new classes, which causes it to generate an unnecessarily large number of classifiers. This paper proposes a modified version of this algorithm, called Learn++.MT that not only reduces the number of classifiers generated, but also provides performance improvements. The out-voting problem, the new algorithm and its promising results on two benchmark datasets as well as on one real world application are presented.

    Original languageEnglish (US)
    Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
    EditorsFabio Roli, Josef Kittler, Terry Windeatt
    PublisherSpringer Verlag
    Pages52-61
    Number of pages10
    ISBN (Print)3540221441, 9783540221449
    DOIs
    StatePublished - 2004

    Publication series

    NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
    Volume3077
    ISSN (Print)0302-9743
    ISSN (Electronic)1611-3349

    All Science Journal Classification (ASJC) codes

    • Theoretical Computer Science
    • Computer Science(all)

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