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 691–700 of 903 papers

TitleStatusHype
Multi-output Classification Framework and Frequency Layer Normalization for Compound Fault Diagnosis in Motor—0
Multi-output Classification using a Cross-talk Architecture for Compound Fault Diagnosis of Motors in Partially Labeled Condition—0
Multi-Output Distributional Fairness via Post-Processing—0
Multi-Participant Multi-Class Vertical Federated Learning—0
Multiple birth least squares support vector machine for multi-class classification—0
Multiple birth support vector machine for multi-class classification—0
Multi-resolution Annotations for Emoji Prediction—0
Multi-Scale Dual-Branch Fully Convolutional Network for Hand Parsing—0
Multi-Task Determinantal Point Processes for Recommendation—0
Multi-Task, Multi-Channel, Multi-Input Learning for Mental Illness Detection using Social Media Text—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
#ModelMetricClaimedVerifiedStatus
1SVM (tficf)Macro F173.9—Unverified
#ModelMetricClaimedVerifiedStatus
1Extra TreesF1-Score93.36—Unverified
#ModelMetricClaimedVerifiedStatus
1Multi-Model EnsembleMean AUC0.99—Unverified