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 501–510 of 903 papers

TitleStatusHype
Resource-constrained Federated Edge Learning with Heterogeneous Data: Formulation and Analysis—0
Resource-Efficient Wearable Computing for Real-Time Reconfigurable Machine Learning: A Cascading Binary Classification—0
Beyond Triplet Loss: Meta Prototypical N-tuple Loss for Person Re-identification—0
Retrieval of surgical phase transitions using reinforcement learning—0
Risk Bounds and Calibration for a Smart Predict-then-Optimize Method—0
Road Mapping In LiDAR Images Using A Joint-Task Dense Dilated Convolutions Merging Network—0
Dimension-Robust MCMC in Bayesian Inverse Problems—0
Robust Nonparametric Regression with Metric-Space Valued Output—0
Rolling Riemannian Manifolds to Solve the Multi-class Classification Problem—0
Scalable Cross Validation Losses for Gaussian Process Models—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