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 541–550 of 903 papers

TitleStatusHype
Generative Adversarial Networks based on Mixed-Attentions for Citation Intent Classification in Scientific Publications—0
Generative-Discriminative Variational Model for Visual Recognition—0
Genetic Column Generation for Computing Lower Bounds for Adversarial Classification—0
Geolocation with Attention-Based Multitask Learning Models—0
GJG@TamilNLP-ACL2022: Emotion Analysis and Classification in Tamil using Transformers—0
GJG@TamilNLP-ACL2022: Using Transformers for Abusive Comment Classification in Tamil—0
Global Capacity Measures for Deep ReLU Networks via Path Sampling—0
GMM-Based Synthetic Samples for Classification of Hyperspectral Images With Limited Training Data—0
Granular Ball K-Class Twin Support Vector Classifier—0
Graph-Based Automatic Feature Selection for Multi-Class Classification via Mean Simplified Silhouette—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