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 451–460 of 903 papers

TitleStatusHype
Overview of the Fifth Social Media Mining for Health Applications (#SMM4H) Shared Tasks at COLING 2020—0
Overview of the Sixth Social Media Mining for Health Applications (#SMM4H) Shared Tasks at NAACL 2021—0
PAC-Bayesian Generalization Bound on Confusion Matrix for Multi-Class Classification—0
Paraphrase and Aggregate with Large Language Models for Minimizing Intent Classification Errors—0
Performance-Guided LLM Knowledge Distillation for Efficient Text Classification at Scale—0
Performance Improvement in Multi-class Classification via Automated Hierarchy Generation and Exploitation through Extended LCPN Schemes—0
Personalized Federated Learning with Exact Stochastic Gradient Descent—0
Phrase-level Self-Attention Networks for Universal Sentence Encoding—0
Pitfalls of Assessing Extracted Hierarchies for Multi-Class Classification—0
PMP-Swin: Multi-Scale Patch Message Passing Swin Transformer for Retinal Disease Classification—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