Multi-label Ensemble Learning

Multi-Label Ensemble Learning is an ensemble learning paradigm that combines multiple base learners to address multi-label classification tasks, where each instance can be associated with a subset of simultaneously relevant labels, distinguishing it from traditional single-label ensemble methods by explicitly modeling label correlations and dependencies.

121

Publications

10.9K

Citations

379

Authors

184

Institutions

Publications per year

2017–2026

66

Authors

379

  1. GT
    Grigorios Tsoumakas

    Aristotle University of Thessaloniki, Greece

    7 publications · 176 citations

    7

    h-index

  2. GL
    Guozheng Li

    Tongji University, China

    5 publications · 161 citations

    5

    h-index

  3. SV
    Sebastián Ventura

    University of Córdoba, Spain

    5 publications · 226 citations

    5

    h-index

  4. JM
    Jose M. Moyano

    University of Córdoba, Spain

    4 publications · 213 citations

    4

    h-index

  5. AZ
    Abbas Z. Kouzani

    Deakin University, Australia

    3 publications · 68 citations

    3

    h-index

1–5 of 379

Venues

  1. Pattern Recognition

    6 publications · 1.1K citations

    6

    h-index

  2. IEEE Access

    6 publications · 370 citations

    6

    h-index

  3. Information Fusion

    5 publications · 309 citations

    5

    h-index

  4. Expert Systems with Applications

    5 publications · 320 citations

    5

    h-index

  5. arXiv (Cornell University)

    Repository · 5 publications · 99 citations

    5

    h-index

1–5