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Dermatology dataset and benchmarks · SOTA2 Research

Research Datasets

Dermatology

Benchmarks

Task NameDataset NameSOTA MethodSort

Task NameDataset NameSOTA ResultTrendResults

Confidence Calibration

dermatology

0.013Confidence Calibration Error

66

dermatology

98.49Accuracy

26

dermatology

0.92AMI

26

Multi-view Clustering

Dermatology

95.25Accuracy

24

Multi-class Classification

Dermatology

98.54F1-score

15

Sparse Feature Selection

Dermatology (test)

97.94F1 Score (%)

14

Stationary Clustering

Dermatology

66.1ARI

13

Nonstationary backward transfer

Dermatology

-0.088BWT-ARI

12

Multiclass Classification

dermatology

97.3Weighted F1

9

Multiclass imbalanced classification

dermatology

0.999AUC

9

Multiclass imbalanced classification

dermatology

97.3Accuracy

9

Multiclass Imbalanced Classification

dermatology

1G-Mean

9

Multiclass Classification

Dermatology (test)

97.94F1 Score

9

Medical image reconstruction

Dermatology

22.27rFID

8

Incremental Clustering

Dermatology

0.56Avg Inc ARI

7

Plane Clustering

Dermatology UCI

0.001CPU Time (s)

7

Dermatology (UCI)

74.73Accuracy

7

Dermatology (5-fold cross-validation)

86.87Accuracy

7

Image Classification

Dermatology

56.76Accuracy

7

Dimensionality Reduction

Dermatology

0.019Runtime (s)

5

kNN Classification

Dermatology

98.1Accuracy

5

Manifold Learning

Dermatology

96.16Performance ((TW + CN)/2)

5

dermatology Out-of-sample

15.66Mean Error

4

Dermatology In-sample

13.27Mean Error

4

Dermatology large variance

0.311NP1

4

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