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 821–830 of 903 papers

TitleStatusHype
Hashtag Recommendation Using End-To-End Memory Networks with Hierarchical Attention—0
Node-Adapt, Path-Adapt and Tree-Adapt:Model-Transfer Domain Adaptation for Random Forest—0
Big Models for Big Data using Multi objective averaged one dependence estimators—0
Simultaneous Learning of Trees and Representations for Extreme Classification and Density Estimation—0
Post Selection Inference with Kernels—0
A Novel Progressive Learning Technique for Multi-class Classification—0
A novel online multi-label classifier for high-speed streaming data applications—0
A Novel Online Real-time Classifier for Multi-label Data Streams—0
A High Speed Multi-label Classifier based on Extreme Learning Machines—0
Multi-Label Classification Method 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
#ModelMetricClaimedVerifiedStatus
1SVM (tficf)Macro F173.9—Unverified
#ModelMetricClaimedVerifiedStatus
1Extra TreesF1-Score93.36—Unverified
#ModelMetricClaimedVerifiedStatus
1Multi-Model EnsembleMean AUC0.99—Unverified