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 351–360 of 903 papers

TitleStatusHype
A Multi-In and Multi-Out Dendritic Neuron Model and its Optimization—0
Additional Look into GAN-based Augmentation for Deep Learning COVID-19 Image Classification—0
A Comparative Analysis of Machine Learning Techniques for IoT Intrusion Detection—0
Counterfactual Explanations for Predictive Business Process Monitoring—0
Correlation-based construction of neighborhood and edge features—0
ATESA-BÆRT: A Heterogeneous Ensemble Learning Model for Aspect-Based Sentiment Analysis—0
Convolutional Neural Networks in Multi-Class Classification of Medical Data—0
Convergence rates of sub-sampled Newton methods—0
A Survey on Open Set Recognition—0
Convergence Rates of Active Learning for Maximum Likelihood Estimation—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