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 881–890 of 903 papers

TitleStatusHype
Multi-borders classification—0
Fast Recursive Multi-class Classification of Pairs of Text Entities for Biomedical Event Extraction—0
Sub-Classifier Construction for Error Correcting Output Code Using Minimum Weight Perfect Matching—0
Correlation-based construction of neighborhood and edge features—0
Learning Kernels Using Local Rademacher Complexity—0
Fast Training of Effective Multi-class Boosting Using Coordinate Descent Optimization—0
Biomedical Event Extraction by Multi-class Classification of Pairs of Text Entities—0
A scalable stage-wise approach to large-margin multi-class loss based boosting—0
Rolling Riemannian Manifolds to Solve the Multi-class Classification Problem—0
Sparse Output Coding for Large-Scale Visual Recognition—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
#ModelMetricClaimedVerifiedStatus
1SVM (tficf)Macro F173.9—Unverified
#ModelMetricClaimedVerifiedStatus
1Extra TreesF1-Score93.36—Unverified
#ModelMetricClaimedVerifiedStatus
1Multi-Model EnsembleMean AUC0.99—Unverified