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 611–620 of 903 papers

TitleStatusHype
MixMOOD: A systematic approach to class distribution mismatch in semi-supervised learning using deep dataset dissimilarity measuresCode0
HMIC: Hierarchical Medical Image Classification, A Deep Learning Approach—0
Adaptive Gradient Methods Converge Faster with Over-Parameterization (but you should do a line-search)Code0
Beyond Triplet Loss: Meta Prototypical N-tuple Loss for Person Re-identification—0
Achieving Equalized Odds by Resampling Sensitive AttributesCode0
On the Equivalence between Online and Private Learnability beyond Binary Classification—0
A Framework for Behavioral Biometric Authentication using Deep Metric Learning on Mobile Devices—0
Minimizing Supervision in Multi-label Categorization—0
AutoMSC: Automatic Assignment of Mathematics Subject Classification LabelsCode0
Evolutionary Simplicial Learning as a Generative and Compact Sparse Framework for Classification—0
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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
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1Extra TreesF1-Score93.36—Unverified
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1Multi-Model EnsembleMean AUC0.99—Unverified