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 321–330 of 903 papers

TitleStatusHype
Fast Bayesian Inference for Non-Conjugate Gaussian Process Regression—0
Anomaly Detection using Ensemble Classification and Evidence Theory—0
Evolutionary Simplicial Learning as a Generative and Compact Sparse Framework for Classification—0
Classification based on Topological Data Analysis—0
Fast Recursive Multi-class Classification of Pairs of Text Entities for Biomedical Event Extraction—0
Explainable Multi-class Classification of Medical Data—0
Explainable Multi-class Classification of the CAMH COVID-19 Mental Health Data—0
Classification with many classes: challenges and pluses—0
Explicit Facial Expression Transfer via Fine-Grained Representations—0
Breaking the Token Barrier: Chunking and Convolution for Efficient Long Text Classification with BERT—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
#ModelMetricClaimedVerifiedStatus
1SVM (tficf)Macro F173.9—Unverified
#ModelMetricClaimedVerifiedStatus
1Extra TreesF1-Score93.36—Unverified
#ModelMetricClaimedVerifiedStatus
1Multi-Model EnsembleMean AUC0.99—Unverified