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 371–380 of 903 papers

TitleStatusHype
GJG@TamilNLP-ACL2022: Using Transformers for Abusive Comment Classification in Tamil—0
SSN_MLRG3 @LT-EDI-ACL2022-Depression Detection System from Social Media Text using Transformer Models—0
SSN_MLRG1@LT-EDI-ACL2022: Multi-Class Classification using BERT models for Detecting Depression Signs from Social Media Text—0
Logically at the Constraint 2022: Multimodal role labelling—0
VISTA: Vision Transformer enhanced by U-Net and Image Colorfulness Frame Filtration for Automatic Retail CheckoutCode1
"Flux+Mutability": A Conditional Generative Approach to One-Class Classification and Anomaly Detection—0
Wound Severity Classification using Deep Neural Network—0
Few-Shot Transfer Learning to improve Chest X-Ray pathology detection using limited tripletsCode0
The Tree Loss: Improving Generalization with Many Classes—0
Prognostic classification based on random convolutional kernel—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