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 141–150 of 903 papers

TitleStatusHype
A Survey on Open Set Recognition—0
A multi-class structured dictionary learning method using discriminant atom selection—0
A Stutter Seldom Comes Alone -- Cross-Corpus Stuttering Detection as a Multi-label Problem—0
Aspect category learning and sentimental analysis using weakly supervised learning—0
A Data-Driven Pool Strategy for Price-Makers Under Imperfect Information—0
Combining features on vertical ground reaction force signal analysis for multiclass diagnosing neurodegenerative diseases—0
A simple technique for improving multi-class classification with neural networks—0
Efficient Malicious UAV Detection Using Autoencoder-TSMamba Integration—0
Classification and Detection in Mammograms with Weak Supervision via Dual Branch Deep Neural Net—0
A scalable stage-wise approach to large-margin multi-class loss based boosting—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