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 61–70 of 903 papers

TitleStatusHype
Granular Ball K-Class Twin Support Vector Classifier—0
Does your model understand genes? A benchmark of gene properties for biological and text modelsCode1
An In-Depth Examination of Risk Assessment in Multi-Class Classification Algorithms—0
Learning from Concealed LabelsCode0
FD-LLM: Large Language Model for Fault Diagnosis of Machines—0
Bi-Band ECoGNet for ECoG Decoding on Classification Task—0
Training Multi-Layer Binary Neural Networks With Local Binary Error Signals—0
Breast Tumor Classification Using EfficientNet Deep Learning ModelCode0
Comparative Analysis of Resource-Efficient CNN Architectures for Brain Tumor Classification—0
A Data-Driven Pool Strategy for Price-Makers Under Imperfect Information—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
#ModelMetricClaimedVerifiedStatus
1Multi-Model EnsembleMean AUC0.99—Unverified