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 721–730 of 903 papers

TitleStatusHype
Single-Label Multi-Class Image Classification by Deep Logistic Regression—0
Deep Auto-Set: A Deep Auto-Encoder-Set Network for Activity Recognition Using Wearables—0
Streaming Network Embedding through Local Actions—0
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
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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
#ModelMetricClaimedVerifiedStatus
1SVM (tficf)Macro F173.9—Unverified
#ModelMetricClaimedVerifiedStatus
1Extra TreesF1-Score93.36—Unverified
#ModelMetricClaimedVerifiedStatus
1Multi-Model EnsembleMean AUC0.99—Unverified