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 781–790 of 903 papers

TitleStatusHype
Deep Learning-Based Intra Mode Derivation for Versatile Video Coding—0
Deep Multi Label Classification in Affine Subspaces—0
DeepNAT: Deep Convolutional Neural Network for Segmenting Neuroanatomy—0
Deep reinforced active learning for multi-class image classification—0
Deep Sequence Models for Text Classification Tasks—0
Deep sparse multi-task learning for feature selection in Alzheimer’s disease diagnosis—0
Deep Transfer Learning for Brain Magnetic Resonance Image Multi-class Classification—0
Degrees of Freedom in Deep Neural Networks—0
Problem-Dependent Power of Quantum Neural Networks on Multi-Class Classification—0
Dermoscopic Image Analysis for ISIC Challenge 2018—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
#ModelMetricClaimedVerifiedStatus
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