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 641–650 of 903 papers

TitleStatusHype
Advancing Efficient Brain Tumor Multi-Class Classification -- New Insights from the Vision Mamba Model in Transfer Learning—0
Adversarial Learning of Label Dependency: A Novel Framework for Multi-class Classification—0
Adversarial Multi-Binary Neural Network for Multi-class Classification—0
Affinity-Based Hierarchical Learning of Dependent Concepts for Human Activity Recognition—0
A Fully Memristive Spiking Neural Network with Unsupervised Learning—0
A Gegenbauer Neural Network with Regularized Weights Direct Determination for Classification—0
A Generalization Error Bound for Multi-class Domain Generalization—0
A generalized flow for multi-class and binary classification tasks: An Azure ML approach—0
A Generative Restricted Boltzmann Machine Based Method for High-Dimensional Motion Data Modeling—0
A High Speed Multi-label Classifier based on Extreme Learning Machines—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