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 771–780 of 903 papers

TitleStatusHype
Deep Attention Model for Triage of Emergency Department Patients—0
Model-Agnostic Private Learning via Stability—0
Dimension-Robust MCMC in Bayesian Inverse Problems—0
Cross-domain Recommendation via Deep Domain Adaptation—0
Cognitive Radar Antenna Selection via Deep Learning—0
Bayes-optimal Hierarchical Classification over Asymmetric Tree-Distance Loss—0
Enhancing Multi-Class Classification of Random Forest using Random Vector Functional Neural Network and Oblique Decision Surfaces—0
Evaluating approaches for supervised semantic labelingCode0
Solving for multi-class using orthogonal coding matrices—0
Binary output layer of feedforward neural networks for solving 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
#ModelMetricClaimedVerifiedStatus
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