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 311–320 of 903 papers

TitleStatusHype
Automatic Detection of Knee Joints and Quantification of Knee Osteoarthritis Severity using Convolutional Neural Networks—0
Deep Multi Label Classification in Affine Subspaces—0
Automatic Classification of Functional Gait Disorders—0
Deep Learning-Based Intra Mode Derivation for Versatile Video Coding—0
Deep Learning-based automated classification of Chinese Speech Sound Disorders—0
Automated Multi-Label Classification based on ML-Plan—0
Deep Learning Approaches for Blood Disease Diagnosis Across Hematopoietic Lineages—0
Automated Fact-Checking of Claims in Argumentative Parliamentary Debates—0
Analysis of the Effect of Unexpected Outliers in the Classification of Spectroscopy Data—0
Deep Convolutional Neural Network based Classification of Alzheimer's Disease using MRI data—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