Threshold-based Fusion

Threshold-Based Fusion is a decision-level fusion strategy that integrates individual classifier outputs by applying a predefined threshold criterion to determine the final classification outcome, thereby distinguishing it from other fusion methods through its reliance on fixed or adaptive thresholding for decision combination.

37

Publications

1.8K

Citations

109

Authors

59

Institutions

Publications per year

2017–2026

10

Authors

109

  1. EP
    Edward Peh

    Institute for Infocomm Research, Singapore

    2 publications · 207 citations

    2

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  2. YL
    Ying‐Chang Liang

    University of Electronic Science and Technology of China, China

    2 publications · 207 citations

    2

    h-index

  3. YL
    Yong Liang Guan

    Nanyang Technological University, Singapore

    2 publications · 207 citations

    2

    h-index

  4. NS
    Nageswara S. V. Rao

    Oak Ridge National Laboratory, United States

    2 publications · 71 citations

    2

    h-index

  5. AK
    Ashish Kumar Bhandari

    National Institute of Technology Patna, India

    2 publications · 49 citations

    2

    h-index

1–5 of 109

Venues

  1. Pattern Recognition

    2 publications · 484 citations

    2

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  2. Multimedia Tools and Applications

    2 publications · 87 citations

    2

    h-index

  3. Applied Soft Computing

    2 publications · 54 citations

    2

    h-index

  4. IEEE Transactions on Multimedia

    1 publications · 128 citations

    1

    h-index

  5. 1

    h-index

1–5