SOTAVerified

Multi-class Classification

Multi-class classification is a type of supervised learning where the goal is to assign an input to one of three or more distinct classes. Unlike binary classification (which has only two classes), multi-class classification handles multiple labels and uses algorithms like logistic regression, decision trees, random forests, SVMs, or neural networks to predict the correct category based on the features of the input data.

Papers

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Showing 111–120 of 903 papers

TitleStatusHype
Regression under demographic parity constraints via unlabeled post-processing—0
Benchmarking deep learning models for bearing fault diagnosis using the CWRU dataset: A multi-label approach—0
Enhanced H-Consistency Bounds—0
Word Embedding Dimension Reduction via Weakly-Supervised Feature SelectionCode0
Uncertainty Calibration with Energy Based Instance-wise Scaling in the Wild DatasetCode1
Weighted Aggregation of Conformity Scores for Classification—0
Non-Robust Features are Not Always Useful in One-Class Classification—0
Described Spatial-Temporal Video Detection—0
Noise-Free Explanation for Driving Action PredictionCode0
Enabling Mixed Effects Neural Networks for Diverse, Clustered Data Using Monte Carlo MethodsCode1
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All datasetsCOVID-19 CXR DatasetCOVID chest X-rayReuters-52TII-SSRC-23Training and validation dataset of capsule vision 2024 challenge.

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1COVID-CXNetAccuracy (%)94.2—Unverified
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1COVID-ResNetF1 score0.9—Unverified
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1SVM (tficf)Macro F173.9—Unverified
#ModelMetricClaimedVerifiedStatus
1Extra TreesF1-Score93.36—Unverified
#ModelMetricClaimedVerifiedStatus
1Multi-Model EnsembleMean AUC0.99—Unverified