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 731–740 of 903 papers

TitleStatusHype
Theoretical Analysis of Adversarial Learning: A Minimax Approach—0
Adversarial Learning of Label Dependency: A Novel Framework for Multi-class Classification—0
Automated Multi-Label Classification based on ML-Plan—0
Automated Fact-Checking of Claims in Argumentative Parliamentary Debates—0
Quantifying Learning Guarantees for Convex but Inconsistent Surrogates—0
DCSVM: Fast Multi-class Classification using Support Vector Machines—0
Machine Learning Methods for Track Classification in the AT-TPCCode0
Concise Explanations of Neural Networks using Adversarial TrainingCode0
Multi-class Classification Model Inspired by Quantum Detection Theory—0
Improved Generalization Bounds for Adversarially Robust Learning—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