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.
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1.9K
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98
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46
Institutions
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2017–2026
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Interactive deduplication using active learning
Sunita Sarawagi, Anuradha Bhamidipaty · 2002 · 693 citations
Mining duplicate questions in stack overflow
Muhammad Ahasanuzzaman, Muhammad Asaduzzaman, Chanchal K. Roy et al. · 2016 · 123 citations
Active sampling for entity matching
Kedar Bellare, Suresh Iyengar, Aditya Parameswaran et al. · 2012 · 92 citations
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Indian Institute of Technology Bombay
Mumbai, India
2 h-index
4 publications
1.5K citations

City University of Hong Kong
Hong Kong, Hong Kong
2 h-index
4 publications
473 citations

Hong Kong University of Science and Technology
Hong Kong, Hong Kong
2 h-index
4 publications
144 citations
Stanford University
Stanford, United States
2 h-index
2 publications
107 citations
Johannes Gutenberg University Mainz
Mainz, Germany
2 h-index
6 publications
66 citations
Yahoo (United States)
Sunnyvale, United States
2 h-index
4 publications
214 citations
Beijing Normal-Hong Kong Baptist University
Zhuhai, China
1 h-index
1 publications
59 citations
Microsoft Research (India)
Bengaluru, India
1 h-index
1 publications
15 citations
1–8 of 46
2 publications · 27 citations
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2 publications · 155 citations
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1 publications · 52 citations
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1 publications · 16 citations
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TOIT · 1 publications · 20 citations
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