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 761–770 of 903 papers

TitleStatusHype
Curriculum Learning for Speech Emotion Recognition from Crowdsourced Labels—0
Multi-Task Determinantal Point Processes for Recommendation—0
Stacked Semantic-Guided Attention Model for Fine-Grained Zero-Shot Learning—0
Localized Multiple Kernel Learning for Anomaly Detection: One-class ClassificationCode0
Several Tunable GMM Kernels—0
Optimal-margin evolutionary classifierCode0
Fusing Structure and Content via Non-negative Matrix Factorization for Embedding Information NetworksCode0
A Recurrent CNN for Automatic Detection and Classification of Coronary Artery Plaque and Stenosis in Coronary CT Angiography—0
Multi-level Activation for Segmentation of Hierarchically-nested Classes—0
The Resistance to Label Noise in K-NN and DNN Depends on its Concentration—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
#ModelMetricClaimedVerifiedStatus
1Extra TreesF1-Score93.36—Unverified
#ModelMetricClaimedVerifiedStatus
1Multi-Model EnsembleMean AUC0.99—Unverified