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 601–610 of 903 papers

TitleStatusHype
sEMG Gesture Recognition with a Simple Model of AttentionCode1
On the Equivalence between Online and Private Learnability beyond Binary Classification—0
Minimizing Supervision in Multi-label Categorization—0
A Framework for Behavioral Biometric Authentication using Deep Metric Learning on Mobile Devices—0
AutoMSC: Automatic Assignment of Mathematics Subject Classification LabelsCode0
On Using Transfer Learning For Plant Disease Detection—0
Evolutionary Simplicial Learning as a Generative and Compact Sparse Framework for Classification—0
Automated detection of COVID-19 cases using deep neural networks with X-ray imagesCode1
Automated diagnosis of COVID-19 with limited posteroanterior chest X-ray images using fine-tuned deep neural networksCode0
Federated Learning with Only Positive LabelsCode0
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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
#ModelMetricClaimedVerifiedStatus
1Multi-Model EnsembleMean AUC0.99—Unverified