Machine Learning Feature Engineering

Machine Learning Feature Engineering is a methodological process in which domain-specific transformations are applied to raw data to construct informative and non-redundant features that enhance the performance of machine learning models in predicting phenotypic outcomes.

305

Publications

16.1K

Citations

1.5K

Authors

703

Institutions

Publications per year

2017–2026

259

Authors

1.5K

  1. BM
    Balachandran Manavalan

    Ajou University, South Korea

    3 publications · 428 citations

    3

    h-index

  2. QZ
    Quan Zou

    University of Electronic Science and Technology of China, China

    3 publications · 199 citations

    3

    h-index

  3. KM
    Kashif Munir

    Khwaja Fareed University of Engineering and Information Technology, Pakistan

    3 publications · 289 citations

    3

    h-index

  4. MS
    Md. Shahriare Satu

    Noakhali Science and Technology University, Bangladesh

    3 publications · 290 citations

    3

    h-index

  5. AR
    Ali Raza

    Khwaja Fareed University of Engineering and Information Technology, Pakistan

    3 publications · 289 citations

    3

    h-index

1–5 of 1.5K

Venues

  1. Scientific Reports

    12 publications · 481 citations

    12

    h-index

  2. PLoS ONE

    11 publications · 347 citations

    11

    h-index

  3. IEEE Access

    10 publications · 815 citations

    10

    h-index

  4. Briefings in Bioinformatics

    7 publications · 448 citations

    7

    h-index

  5. 6

    h-index

1–5