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 551–560 of 903 papers

TitleStatusHype
Stable Learning in Coding Space for Multi-Class Decoding and Its Extension for Multi-Class Hypothesis Transfer Learning—0
Stacked Semantic-Guided Attention Model for Fine-Grained Zero-Shot Learning—0
STAR: Noisy Semi-Supervised Transfer Learning for Visual Classification—0
Stitching Satellites to the Edge: Pervasive and Efficient Federated LEO Satellite Learning—0
Streaming Network Embedding through Local Actions—0
StructBoost: Boosting Methods for Predicting Structured Output Variables—0
Student Performance Prediction with Optimum Multilabel Ensemble Model—0
Sub-Classifier Construction for Error Correcting Output Code Using Minimum Weight Perfect Matching—0
Supervised level-wise pretraining for recurrent neural network initialization in multi-class classification—0
Support matrix machine: A review—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