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 401–410 of 903 papers

TitleStatusHype
Dynamic Spectrum Matching with One-shot Learning—0
Hierarchical Deep Learning with Generative Adversarial Network for Automatic Cardiac Diagnosis from ECG Signals—0
Hierarchical Feature Hashing for Fast Dimensionality Reduction—0
Hierarchical Neyman-Pearson Classification for Prioritizing Severe Disease Categories in COVID-19 Patient Data—0
HMIC: Hierarchical Medical Image Classification, A Deep Learning Approach—0
Dynamic Sentence Boundary Detection for Simultaneous Translation—0
How can we generalise learning distributed representations of graphs?—0
How many faces can be recognized? Performance extrapolation for multi-class classification—0
How optimal transport can tackle gender biases in multi-class neural-network classifiers for job recommendations?—0
Biomarker based Cancer Classification using an Ensemble with Pre-trained Models—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