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 361–370 of 903 papers

TitleStatusHype
Convergence of Uncertainty Sampling for Active Learning—0
A Stutter Seldom Comes Alone -- Cross-Corpus Stuttering Detection as a Multi-label Problem—0
Contrastive Learning for Fair Representations—0
Aspect category learning and sentimental analysis using weakly supervised learning—0
A multi-class structured dictionary learning method using discriminant atom selection—0
Constrained Multi-Layer Contrastive Learning for Implicit Discourse Relationship Recognition—0
Consistency of semi-supervised learning algorithms on graphs: Probit and one-hot methods—0
A simple technique for improving multi-class classification with neural networks—0
Confidence Prediction for Lexicon-Free OCR—0
Confidence Calibration for Domain Generalization under Covariate Shift—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
#ModelMetricClaimedVerifiedStatus
1Multi-Model EnsembleMean AUC0.99—Unverified