Ensemble learning

    Research output: Chapter in Book/Report/Conference proceedingChapter

    205 Scopus citations

    Abstract

    Over the last couple of decades, multiple classifier systems, also called ensemble systems have enjoyed growing attention within the computational intelligence and machine learning community. This attention has been well deserved, as ensemble systems have proven themselves to be very effective and extremely versatile in a broad spectrum of problem domains and real-world applications. Originally developed to reduce the variance—thereby improving the accuracy—of an automated decision-making system, ensemble systems have since been successfully used to address a variety of machine learning problems, such as feature selection, confidence estimation, missing feature, incremental learning, error correction, classimbalanced data, learning concept drift from nonstationary distributions, among others. This chapter provides an overview of ensemble systems, their properties, and how they can be applied to such a wide spectrum of applications.

    Original languageEnglish (US)
    Title of host publicationEnsemble Machine Learning
    Subtitle of host publicationMethods and Applications
    PublisherSpringer US
    Pages1-34
    Number of pages34
    ISBN (Electronic)9781441993267
    ISBN (Print)9781441993250
    DOIs
    StatePublished - Jan 1 2012

    All Science Journal Classification (ASJC) codes

    • Computer Science(all)

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