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 531–540 of 903 papers

TitleStatusHype
Several Tunable GMM Kernels—0
Sexism Identification in Tweets and Gabs using Deep Neural Networks—0
Shallow Domain Adaptive Embeddings for Sentiment Analysis—0
Sheaf HyperNetworks for Personalized Federated Learning—0
SHORE: A Long-term User Lifetime Value Prediction Model in Digital Games—0
Simpson's Bias in NLP Training—0
Simulation-supervised deep learning for analysing organelles states and behaviour in living cells—0
Simultaneous Learning of Trees and Representations for Extreme Classification and Density Estimation—0
Single-Label Multi-Class Image Classification by Deep Logistic Regression—0
Softmax Q-Distribution Estimation for Structured Prediction: A Theoretical Interpretation for RAML—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