Symbolic Feature Engineering

Symbolic Feature Engineering is a methodological contribution in machine learning that, as a form of feature engineering, specifically involves the systematic derivation of new features through symbolic manipulation of polynomial and interaction terms to enhance model expressiveness and interpretability.

37

Publications

2.4K

Citations

105

Authors

59

Institutions

Publications per year

2017–2026

14

Authors

105

  1. DS
    D. S. Guru

    University of Mysore, India

    4 publications · 69 citations

    4

    h-index

  2. HP
    H.N. Prakash

    University of Mysore, India

    3 publications · 59 citations

    3

    h-index

  3. RM
    Renata M.C.R. de Souza

    Universidade Federal de Pernambuco, Brazil

    2 publications · 127 citations

    2

    h-index

  4. EM
    Eric‐Jan Manders

    Vanderbilt University, United States

    2 publications · 52 citations

    2

    h-index

  5. XJ
    Xin Jin

    National Laboratory of the Rockies, United States

    2 publications · 67 citations

    2

    h-index

1–5 of 105

Venues

  1. Expert Systems with Applications

    3 publications · 135 citations

    3

    h-index

  2. Knowledge-Based Systems

    2 publications · 51 citations

    2

    h-index

  3. Pattern Recognition Letters

    2 publications · 97 citations

    2

    h-index

  4. Machine Learning

    2 publications · 1K citations

    2

    h-index

  5. Multimedia Tools and Applications

    1 publications · 114 citations

    1

    h-index

1–5