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 571–580 of 903 papers

TitleStatusHype
Simulation-supervised deep learning for analysing organelles states and behaviour in living cells—0
Neural Neighborhood Encoding for Classification—0
Spatio-Temporal EEG Representation Learning on Riemannian Manifold and Euclidean SpaceCode1
Metrics for Multi-Class Classification: an Overview—0
Residual Generation Using Physically-Based Grey-Box Recurrent Neural Networks For Engine Fault Diagnosis—0
Curriculum learning for improved femur fracture classification: scheduling data with prior knowledge and uncertaintyCode1
Additive interaction modelling using I-priorsCode0
Joint Visual and Temporal Consistency for Unsupervised Domain Adaptive Person Re-Identification—0
Multi-label Contrastive Predictive Coding—0
Provably Consistent Partial-Label Learning—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
#ModelMetricClaimedVerifiedStatus
1SVM (tficf)Macro F173.9—Unverified
#ModelMetricClaimedVerifiedStatus
1Extra TreesF1-Score93.36—Unverified
#ModelMetricClaimedVerifiedStatus
1Multi-Model EnsembleMean AUC0.99—Unverified