Binary Churn Classification

Binary Churn Classification is a supervised machine learning task that categorizes customers into one of two mutually exclusive states—churned or non-churned—based on historical behavioral and transactional data to predict customer attrition in a binary outcome framework.

9

Publications

225

Citations

28

Authors

8

Institutions

Publications per year

2017–2026

4

Authors

28

  1. SA
    Sajid Anwar

    Institute of Management Sciences Peshawar, Pakistan

    1 publications · 15 citations

    1

    h-index

  2. YZ
    Yangming Zhang

    Beihang University, China

    1 publications · 30 citations

    1

    h-index

  3. DV
    Dirk Van den Poel

    Ghent University, Belgium

    1 publications · 11 citations

    1

    h-index

  4. SB
    Seema Baghla

    Punjabi University, India

    1 publications · 35 citations

    1

    h-index

  5. AA
    Andraš Anderla

    University of Novi Sad, Serbia

    1 publications · 21 citations

    1

    h-index

1–5 of 28

Venues

  1. Applied Sciences

    1 publications · 21 citations

    1

    h-index

  2. International Journal of Database Theory and Application

    IJDTA · 1 publications · 20 citations

    1

    h-index

  3. RePEc: Research Papers in Economics

    Repository · 1 publications · 11 citations

    1

    h-index

  4. IEEE Transactions on Games

    TG · 1 publications · 42 citations

    1

    h-index

  5. Microprocessors and Microsystems

    MICPRO · 1 publications · 35 citations

    1

    h-index

1–5