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 471–480 of 903 papers

TitleStatusHype
Resource-constrained Federated Edge Learning with Heterogeneous Data: Formulation and Analysis—0
Query and Extract: Refining Event Extraction as Type-oriented Binary Decoding—0
Distribution-Free Federated Learning with Conformal Predictions—0
Instance-based Label Smoothing For Better Calibrated Classification NetworksCode0
Pairwise Margin Maximization for Deep Neural NetworksCode0
Measure Twice, Cut Once: Quantifying Bias and Fairness in Deep Neural Networks—0
Generative Adversarial Networks based on Mixed-Attentions for Citation Intent Classification in Scientific Publications—0
Introducing the DOME Activation Functions—0
Improving the Accuracy of Learning Example Weights for Imbalance Classification—0
Multi-loss ensemble deep learning for chest X-ray 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