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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 201–210 of 903 papers

TitleStatusHype
Convergence of Uncertainty Sampling for Active Learning—0
Convergence Rates of Active Learning for Maximum Likelihood Estimation—0
Convergence rates of sub-sampled Newton methods—0
Convolutional Neural Networks in Multi-Class Classification of Medical Data—0
Correlation-based construction of neighborhood and edge features—0
Counterfactual Explanations for Predictive Business Process Monitoring—0
COV-ELM classifier: An Extreme Learning Machine based identification of COVID-19 using Chest X-Ray Images—0
Deep Convolutional Neural Network based Classification of Alzheimer's Disease using MRI data—0
Additional Look into GAN-based Augmentation for Deep Learning COVID-19 Image Classification—0
Deep Learning-based automated classification of Chinese Speech Sound Disorders—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