• myGriffith
    • Staff portal
    • Contact Us⌄
      • Future student enquiries 1800 677 728
      • Current student enquiries 1800 154 055
      • International enquiries +61 7 3735 6425
      • General enquiries 07 3735 7111
      • Online enquiries
      • Staff phonebook
    View Item 
    •   Home
    • Griffith Research Online
    • Conference outputs
    • View Item
    • Home
    • Griffith Research Online
    • Conference outputs
    • View Item
    JavaScript is disabled for your browser. Some features of this site may not work without it.

    Browse

  • All of Griffith Research Online
    • Communities & Collections
    • Authors
    • By Issue Date
    • Titles
  • This Collection
    • Authors
    • By Issue Date
    • Titles
  • Statistics

  • Most Popular Items
  • Statistics by Country
  • Most Popular Authors
  • Support

  • Contact us
  • FAQs
  • Admin login

  • Login
  • A Homogeneous-Heterogeneous Ensemble of Classifiers

    Thumbnail
    View/Open
    Embargoed until: 2021-11-17
    Author(s)
    Luong, AV
    Vu, TH
    Nguyen, PM
    Van Pham, N
    McCall, J
    Liew, AWC
    Nguyen, TT
    Griffith University Author(s)
    Luong, Vu A.
    Liew, Alan Wee-Chung
    Year published
    2020
    Metadata
    Show full item record
    Abstract
    In this study, we introduce an ensemble system by combining homogeneous ensemble and heterogeneous ensemble into a single framework. Based on the observation that the projected data is significantly different from the original data as well as each other after using random projections, we construct the homogeneous module by applying random projections on the training data to obtain the new training sets. In the heterogeneous module, several learning algorithms will train on the new training sets to generate the base classifiers. We propose four combining algorithms based on Sum Rule and Majority Vote Rule for the proposed ...
    View more >
    In this study, we introduce an ensemble system by combining homogeneous ensemble and heterogeneous ensemble into a single framework. Based on the observation that the projected data is significantly different from the original data as well as each other after using random projections, we construct the homogeneous module by applying random projections on the training data to obtain the new training sets. In the heterogeneous module, several learning algorithms will train on the new training sets to generate the base classifiers. We propose four combining algorithms based on Sum Rule and Majority Vote Rule for the proposed ensemble. Experiments on some popular datasets confirm that the proposed ensemble method is better than several well-known benchmark algorithms proposed framework has great flexibility when applied to real-world applications. The proposed framework has great flexibility when applied to real-world applications by using any techniques that make rich training data for the homogeneous module, as well as using any set of learning algorithms for the heterogeneous module.
    View less >
    Conference Title
    Communications in Computer and Information Science
    Volume
    1333
    DOI
    https://doi.org/10.1007/978-3-030-63823-8_30
    Copyright Statement
    © Springer Nature Switzerland AG 2020. This is the author-manuscript version of this paper. Reproduced in accordance with the copyright policy of the publisher.The original publication is available at www.springerlink.com
    Subject
    Neural, Evolutionary and Fuzzy Computation
    Publication URI
    http://hdl.handle.net/10072/401137
    Collection
    • Conference outputs

    Footer

    Disclaimer

    • Privacy policy
    • Copyright matters
    • CRICOS Provider - 00233E

    Tagline

    • Gold Coast
    • Logan
    • Brisbane - Queensland, Australia
    First Peoples of Australia
    • Aboriginal
    • Torres Strait Islander