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
An Infinite-Feature Extension for Bayesian ReLU Nets That Fixes Their Asymptotic Overconfidence—0
FLAG: Fast Label-Adaptive Aggregation for Multi-label Classification in Federated Learning—0
Flat and Nested Negation and Uncertainty Detection with PubMed BERT—0
Comparative Analysis of Resource-Efficient CNN Architectures for Brain Tumor Classification—0
"Flux+Mutability": A Conditional Generative Approach to One-Class Classification and Anomaly Detection—0
FOLD-SE: An Efficient Rule-based Machine Learning Algorithm with Scalable Explainability—0
FoLDTree: A ULDA-Based Decision Tree Framework for Efficient Oblique Splits and Feature Selection—0
Comparison of Multi-Class and Binary Classification Machine Learning Models in Identifying Strong Gravitational Lenses—0
FORML: Learning to Reweight Data for Fairness—0
Dysfluencies Seldom Come Alone -- Detection as a Multi-Label Problem—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