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 221–230 of 903 papers

TitleStatusHype
AutoBayes: Automated Bayesian Graph Exploration for Nuisance-Robust Inference—0
DCSVM: Fast Multi-class Classification using Support Vector Machines—0
Classifying Documents within Multiple Hierarchical Datasets using Multi-Task Learning—0
Apple Counting using Convolutional Neural Networks—0
Classified as unknown: A novel Bayesian neural network—0
A Deep Generative Approach to Native Language Identification—0
Deep Attention Model for Triage of Emergency Department Patients—0
Deep Auto-Set: A Deep Auto-Encoder-Set Network for Activity Recognition Using Wearables—0
Automated diagnosis of lung diseases using vision transformer: a comparative study on chest x-ray classification—0
Classification with many classes: challenges and pluses—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