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 851–860 of 903 papers

TitleStatusHype
Faint Features Tell: Automatic Vertebrae Fracture Screening Assisted by Contrastive Learning—0
Fast Bayesian Inference for Non-Conjugate Gaussian Process Regression—0
Fast Recursive Multi-class Classification of Pairs of Text Entities for Biomedical Event Extraction—0
Fast Training of Effective Multi-class Boosting Using Coordinate Descent Optimization—0
FD-LLM: Large Language Model for Fault Diagnosis of Machines—0
Feature-aware conditional GAN for category text generation—0
Feature Incay for Representation Regularization—0
FedFACT: A Provable Framework for Controllable Group-Fairness Calibration in Federated Learning—0
Cross-System Categorization of Abnormal Traces in Microservice-Based Systems via Meta-Learning—0
Filtering Aggression from the Multilingual Social Media Feed—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