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 341–350 of 903 papers

TitleStatusHype
Graph-based Extreme Feature Selection for Multi-class Classification Tasks—0
FLAG: Fast Label-Adaptive Aggregation for Multi-label Classification in Federated Learning—0
How optimal transport can tackle gender biases in multi-class neural-network classifiers for job recommendations?—0
Characterizing the Optimal 0-1 Loss for Multi-class Classification with a Test-time Attacker—0
Occupant's Behavior and Emotion Based Indoor Environment's Illumination Regulation—0
Optimal Transport for Change Detection on LiDAR Point CloudsCode0
Capsules as viewpoint learners for human pose estimation—0
Cut your Losses with Squentropy—0
Conformalized Semi-supervised Random Forest for Classification and Abnormality DetectionCode0
Classified as unknown: A novel Bayesian neural network—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