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 701–710 of 903 papers

TitleStatusHype
Neural-based Tamil Grammar Error Detection—0
NeuralClassifier: An Open-source Neural Hierarchical Multi-label Text Classification Toolkit—0
Neural Collapse versus Low-rank Bias: Is Deep Neural Collapse Really Optimal?—0
Neural Neighborhood Encoding for Classification—0
Neural Network Learning and Quantum Gravity—0
New Bounds on the Accuracy of Majority Voting for Multi-Class Classification—0
Neyman-Pearson Multi-class Classification via Cost-sensitive Learning—0
Node-Adapt, Path-Adapt and Tree-Adapt:Model-Transfer Domain Adaptation for Random Forest—0
No Multiplication? No Floating Point? No Problem! Training Networks for Efficient Inference—0
Non-Robust Features are Not Always Useful in One-Class 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