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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 421–430 of 903 papers

TitleStatusHype
Comparison of Multi-Class and Binary Classification Machine Learning Models in Identifying Strong Gravitational Lenses—0
A Recurrent CNN for Automatic Detection and Classification of Coronary Artery Plaque and Stenosis in Coronary CT Angiography—0
Improving Low-Resource Named Entity Recognition using Joint Sentence and Token Labeling—0
Improving Primate Sounds Classification using Binary Presorting for Deep Learning—0
Improving the Accuracy of Learning Example Weights for Imbalance Classification—0
A High Speed Multi-label Classifier based on Extreme Learning Machines—0
FOLD-SE: An Efficient Rule-based Machine Learning Algorithm with Scalable Explainability—0
"Flux+Mutability": A Conditional Generative Approach to One-Class Classification and Anomaly Detection—0
Comparison of Decision Tree Based Classification Strategies to Detect External Chemical Stimuli from Raw and Filtered Plant Electrical Response—0
Flat and Nested Negation and Uncertainty Detection with PubMed BERT—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