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 391–400 of 903 papers

TitleStatusHype
Generalized Conditional Gradient for Sparse Estimation—0
Graph Convolutional Networks for Classification with a Structured Label Space—0
Computer Aided Detection of Anemia-like Pallor—0
Armijo Line-search Can Make (Stochastic) Gradient Descent Provably Faster—0
Generalization and Risk Bounds for Recurrent Neural Networks—0
Harnessing Deep Learning and Satellite Imagery for Post-Buyout Land Cover Mapping—0
Generalization Analysis on Learning with a Concurrent Verifier—0
ComplAI: Theory of A Unified Framework for Multi-factor Assessment of Black-Box Supervised Machine Learning Models—0
Gaussian Processes on Hypergraphs—0
ARGUABLY at ComMA@ICON: Detection of Multilingual Aggressive, Gender Biased, and Communally Charged Tweets Using Ensemble and Fine-Tuned IndicBERT—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