Decision Threshold Selection
Decision Threshold Selection is a methodological process in binary classification systems that determines the optimal cutoff point for distinguishing positive from negative instances by balancing sensitivity and specificity to minimize false positive rates.
222
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12.8K
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730
Authors
357
Institutions
Publications per year
2017–2026
61
Parents
222
Decision feedback equalization
C.A. Belfiore, J.H. Park · Proceedings of the IEEE · 1979 · 409 citations
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357

Chinese Academy of Sciences
Beijing, China
5 h-index
12 publications
502 citations
Ningbo University
Ningbo, China
4 h-index
11 publications
299 citations

Centre National de la Recherche Scientifique
Paris, France
4 h-index
5 publications
185 citations
Princeton University
Princeton, United States
4 h-index
9 publications
1K citations
University of Michigan
Ann Arbor, United States
3 h-index
14 publications
680 citations
Université de Lille
Lille, France
3 h-index
4 publications
128 citations
IBM Research - Thomas J. Watson Research Center
Yorktown Heights, United States
3 h-index
5 publications
78 citations

Victoria University of Wellington
Wellington, New Zealand
2 h-index
4 publications
193 citations
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6 publications · 265 citations
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4 publications · 60 citations
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4 publications · 106 citations
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3 publications · 219 citations
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MDM · 3 publications · 168 citations
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