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 251–260 of 903 papers

TitleStatusHype
Detecting Throat Cancer from Speech Signals using Machine Learning: A Scoping Literature Review—0
Detecting Tweets Reporting Birth Defect Pregnancy Outcome Using Two-View CNN RNN Based Architecture—0
Detection Made Easy: Potentials of Large Language Models for Solidity Vulnerabilities—0
Detection of Suicidal Risk on Social Media: A Hybrid Model—0
Classification of Operational Records in Aviation Using Deep Learning Approaches—0
Classification based on Topological Data Analysis—0
A One-Sided Classification Toolkit with Applications in the Analysis of Spectroscopy Data—0
Characterizing the Optimal 0-1 Loss for Multi-class Classification with a Test-time Attacker—0
An Unsupervised Domain-Independent Framework for Automated Detection of Persuasion Tactics in Text—0
A Generalization Error Bound for Multi-class Domain Generalization—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