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
Neyman-Pearson Multi-class Classification via Cost-sensitive Learning—0
Sexism Identification in Tweets and Gabs using Deep Neural Networks—0
Co-attention network with label embedding for text classificationCode1
On the Effectiveness of Interpretable Feedforward Neural Network—0
TorchXRayVision: A library of chest X-ray datasets and modelsCode2
Convergence of Uncertainty Sampling for Active Learning—0
Analysis of French Phonetic Idiosyncrasies for Accent RecognitionCode0
Resource-constrained Federated Edge Learning with Heterogeneous Data: Formulation and Analysis—0
Query and Extract: Refining Event Extraction as Type-oriented Binary Decoding—0
Distribution-Free Federated Learning with Conformal Predictions—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