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 441–450 of 903 papers

TitleStatusHype
Multi-class granular approximation by means of disjoint and adjacent fuzzy granules—0
Incremental user embedding modeling for personalized text classification—0
FORML: Learning to Reweight Data for Fairness—0
Learning Optimal Topology for Ad-hoc Robot Networks—0
Polyphonic audio event detection: multi-label or multi-class multi-task classification problem?—0
Self-Supervision and Multi-Task Learning: Challenges in Fine-Grained COVID-19 Multi-Class Classification from Chest X-rays—0
The SAMME.C2 algorithm for severely imbalanced multi-class classification—0
Label Distributionally Robust Losses for Multi-class Classification: Consistency, Robustness and AdaptivityCode0
Scalable Batch-Mode Deep Bayesian Active Learning via Equivalence Class AnnealingCode0
Contrastive Learning for Fair Representations—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