Automated Machine Learning

Automated Machine Learning is a predictive modeling framework that distinguishes itself by systematically integrating and optimizing machine learning pipeline components—such as feature preprocessing, model selection, hyperparameter tuning, and validation—through algorithmic search and evaluation, thereby minimizing manual intervention in the model development process.

1.8K

Publications

104.8K

Citations

7.4K

Authors

2.3K

Institutions

Publications per year

2017–2026

1.5K

Authors

7.4K

  1. IT
    Ioannis Tsamardinos

    University of Crete, Greece

    9 publications · 359 citations

    10

    h-index

  2. JH
    Jason H. Moore

    Dartmouth College, United States

    9 publications · 1.3K citations

    9

    h-index

  3. FH
    Frank Hutter

    University of Freiburg, Germany

    8 publications · 3K citations

    8

    h-index

  4. MW
    Marcel Wever

    Paderborn University, Germany

    8 publications · 406 citations

    8

    h-index

  5. DW
    Dakuo Wang

    Northeastern University, United States

    8 publications · 379 citations

    8

    h-index

1–5 of 7.4K

Venues

  1. arXiv (Cornell University)

    Repository · 84 publications · 3.8K citations

    33

    h-index

  2. Scientific Reports

    32 publications · 1.4K citations

    23

    h-index

  3. IEEE Access

    28 publications · 3.7K citations

    22

    h-index

  4. Sensors

    20 publications · 1.3K citations

    18

    h-index

  5. PLoS ONE

    18 publications · 1.3K citations

    15

    h-index

1–5