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 351–360 of 903 papers

TitleStatusHype
Towards Adversarial Realism and Robust Learning for IoT Intrusion Detection and Classification—0
Conformal inference is (almost) free for neural networks trained with early stoppingCode0
Increasing Fairness via Combination with Learning Guarantees—0
ComplAI: Theory of A Unified Framework for Multi-factor Assessment of Black-Box Supervised Machine Learning Models—0
Problem-Dependent Power of Quantum Neural Networks on Multi-Class Classification—0
Anomaly Detection using Ensemble Classification and Evidence Theory—0
Learning Disentangled Label Representations for Multi-label Classification—0
Semi-supervised binary classification with latent distance learning—0
X-PuDu at SemEval-2022 Task 7: A Replaced Token Detection Task Pre-trained Model with Pattern-aware Ensembling for Identifying Plausible Clarifications—0
Condensed Gradient BoostingCode0
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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