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 421–430 of 903 papers

TitleStatusHype
Improving Disease Detection from Social Media Text via Self-Augmentation and Contrastive Learning—0
CPS Attack Detection under Limited Local Information in Cyber Security: A Multi-node Multi-class Classification Ensemble Approach—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
Cross-domain Recommendation via Deep Domain Adaptation—0
Dysfluencies Seldom Come Alone -- Detection as a Multi-Label Problem—0
Revisiting Classification Perspective on Scene Text Recognition—0
Biomedical Event Extraction by Multi-class Classification of Pairs of Text Entities—0
Affinity-Based Hierarchical Learning of Dependent Concepts for Human Activity Recognition—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