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 571–580 of 903 papers

TitleStatusHype
Improved Generalization Bounds for Adversarially Robust Learning—0
Improving automated segmentation of radio shows with audio embeddings—0
Improving Disease Detection from Social Media Text via Self-Augmentation and Contrastive Learning—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
In-Context Learning for Label-Efficient Cancer Image Classification in Oncology—0
Increasing Fairness via Combination with Learning Guarantees—0
Incremental user embedding modeling for personalized text classification—0
Inducing a hierarchy for multi-class classification problems—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
#ModelMetricClaimedVerifiedStatus
1Multi-Model EnsembleMean AUC0.99—Unverified