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Multi-Label Classification research benchmarks – Page 14 · SOTA2 Research

Multi-Label Classification

Benchmarks

Dataset NameSOTA MethodSort

Dataset NameSOTA methodMetricTrendResultsLast Updated

0.752Precision (macro)

3

Mar 11, 2026

78.54Macro Precision

3

Mar 11, 2026

Vehicle Error Pattern Classification dataset (test)

80.9AUROC (Micro)

3

Feb 26, 2026

27.34Macro F1

3

Feb 26, 2026

Open-Images v4 (7186/400) (unseen)

81.4mAP (Mean Average Precision)

3

Feb 26, 2026

DBLP PAPERCITATION 90%

62.8Micro-F1

3

Feb 26, 2026

DBLP Papercitation 80%

62.25Micro-F1

3

Feb 26, 2026

DBLP PAPERCITATION 70%

62.21Micro-F1

3

Feb 26, 2026

DBLP PAPERCITATION 60%

62.1Micro-F1

3

Feb 26, 2026

DBLP PAPERCITATION 50%

61.85Micro-F1

3

Feb 26, 2026

DBLP PAPERCITATION 30%

61.46Micro-F1

3

Feb 26, 2026

DBLP PaperCitation (10% train)

60.1Micro-F1

3

Feb 26, 2026

Turkish tweet n = 75 (test)

69.3Macro F1

2

Jun 15, 2026

MIMIC-IV-Notes (source)

66.83Precision

1

Apr 27, 2026

DIATOM FUN (test)

—

—F1 Score

0

Feb 26, 2026

ENRON FUN (test)

—

—F1 Score

0

Feb 26, 2026

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