An ensemble approach for data fusion with Learn++

Michael Lewitt, Robi Polikar

    Research output: Chapter in Book/Report/Conference proceedingChapter

    8 Scopus citations

    Abstract

    We have recently introduced Learn++ as an incremental learning algorithm capable of learning additional data that may later become available. The strength of Learn++ lies with its ability to learn new data without forgetting previously acquired knowledge and without requiring access to any of the previously seen data, even when the new data introduce new classes. Learn++, inspired in pan by AdaBoost, achieves incremental learning through generating an ensemble of classifiers for each new dataset that becomes available and then combining them through weighted majority voting with a distribution update rule modified for incremental learning of new classes. We have recently discovered that Learn++ also provides a practical and a general purpose approach for multisensor and/or multimodality data fusion. In this paper, we present Learn++ as an addition to the new breed of classifier fusion algorithms, along with preliminary results obtained on two real-world data fusion applications.

    Original languageEnglish (US)
    Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
    EditorsTerry Windeatt, Fabio Roli
    PublisherSpringer Verlag
    Pages176-185
    Number of pages10
    ISBN (Print)3540403698, 9783540403692
    DOIs
    StatePublished - 2003

    Publication series

    NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
    Volume2709
    ISSN (Print)0302-9743
    ISSN (Electronic)1611-3349

    All Science Journal Classification (ASJC) codes

    • Theoretical Computer Science
    • Computer Science(all)

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