Binary Relevance
Binary Relevance is a relevance judgment scheme in information retrieval that categorizes document relevance as strictly either relevant or non-relevant, without intermediate gradations.
109
Publications
6.5K
Citations
312
Authors
162
Institutions
Publications per year
2017–2026
53
109
Expected reciprocal rank for graded relevance
Olivier Chapelle, Donald Metlzer, Ya Zhang et al. · 2009 · 839 citations
Binary relevance for multi-label learning: an overview
Min-Ling Zhang, Yukun Li, Xuying Liu et al. · Frontiers of Computer Science · 2017 · 429 citations
Yue Shi, Alexandros Karatzoglou, Linas Baltrunas et al. · 2012 · 309 citations
On label dependence and loss minimization in multi-label classification
Krzysztof Dembczyński, Willem Waegeman, Weiwei Cheng et al. · Machine Learning · 2012 · 292 citations · Full text
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Wrocław University of Science and Technology, Poland
4 publications · 142 citations
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3
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3
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162

Wrocław University of Science and Technology
Wroclaw, Poland
4 h-index
8 publications
268 citations

National Taiwan University
Taipei, Taiwan
4 h-index
6 publications
638 citations

Delft University of Technology
Delft, The Netherlands
3 h-index
9 publications
1.1K citations
University of Udine
Udine, Italy
3 h-index
5 publications
160 citations
Universidade de São Paulo
São Paulo, Brazil
3 h-index
8 publications
475 citations
National University of Singapore
Singapore, Singapore
3 h-index
6 publications
186 citations

Southeast University
Nanjing, China
3 h-index
7 publications
1.8K citations
Telefonica Research and Development
Madrid, Spain
3 h-index
8 publications
1.1K citations
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Repository · 6 publications · 246 citations
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JASIST · 3 publications · 353 citations
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TKDE · 3 publications · 114 citations
3
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2 publications · 84 citations
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2 publications · 53 citations
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