Classification-based Duplicate Detection

Classification-Based Duplicate Detection is a duplicate detection method that employs classification models to determine whether pairs of records refer to the same real-world entity, distinguishing itself from rule-based or similarity-based approaches by leveraging labeled training data to learn decision boundaries indicative of duplication.

31

Publications

1.9K

Citations

98

Authors

46

Institutions

Publications per year

2017–2026

5

Authors

98

  1. SS
    Sunita Sarawagi

    Indian Institute of Technology Bombay, India

    2 publications · 729 citations

    2

    h-index

  2. VR
    Vibhor Rastogi

    University of Washington, United States

    2 publications · 107 citations

    2

    h-index

  3. AB
    Anuradha Bhamidipaty

    IBM Research - India, India

    2 publications · 729 citations

    2

    h-index

  4. KB
    Kedar Bellare

    University of Massachusetts Amherst, United States

    2 publications · 107 citations

    2

    h-index

  5. AP
    Aditya Parameswaran

    Stanford University, United States

    2 publications · 107 citations

    2

    h-index

1–5 of 98

Venues

  1. IEEE Access

    2 publications · 27 citations

    2

    h-index

  2. IEEE Transactions on Multimedia

    2 publications · 155 citations

    2

    h-index

  3. Empirical Software Engineering

    1 publications · 52 citations

    1

    h-index

  4. Journal of Computer Science

    1 publications · 16 citations

    1

    h-index

  5. ACM Transactions on Internet Technology

    TOIT · 1 publications · 20 citations

    1

    h-index

1–5