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 781–790 of 903 papers

TitleStatusHype
Semi-supervised Outlier Detection using Generative And Adversary Framework—0
Extrapolating Expected Accuracies for Large Multi-Class ProblemsCode0
Automatic Classification of Functional Gait Disorders—0
GMM-Based Synthetic Samples for Classification of Hyperspectral Images With Limited Training Data—0
Hierarchical Clustering Beyond the Worst-Case—0
A Semantic Loss Function for Deep Learning with Symbolic KnowledgeCode0
Tensor Decompositions for Modeling Inverse Dynamics—0
3D Shape Classification Using Collaborative Representation based Projections—0
Candidates vs. Noises Estimation for Large Multi-Class Classification Problem—0
Tensor Valued Common and Individual Feature Extraction: Multi-dimensional Perspective—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