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 291–300 of 903 papers

TitleStatusHype
An Effective Approach for Multi-label Classification with Missing Labels—0
Detecting immune cells with label-free two-photon autofluorescence and deep learning—0
Detecting Disengagement in Virtual Learning as an Anomaly using Temporal Convolutional Network Autoencoder—0
Described Spatial-Temporal Video Detection—0
An Attention-based Long Short-Term Memory Framework for Detection of Bitcoin Scams—0
Advancing Efficient Brain Tumor Multi-Class Classification -- New Insights from the Vision Mamba Model in Transfer Learning—0
Dermoscopic Image Analysis for ISIC Challenge 2018—0
Problem-Dependent Power of Quantum Neural Networks on Multi-Class Classification—0
Degrees of Freedom in Deep Neural Networks—0
An Asymptotically Optimal Contextual Bandit Algorithm Using Hierarchical Structures—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