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 491–500 of 903 papers

TitleStatusHype
SphereFace2: Binary Classification is All You Need for Deep Face Recognition—0
Semi-supervised Meta-learning for Cross-domain Few-shot Intent Classification—0
Decision-forest voting scheme for classification of rare classes in network intrusion detection—0
E-PixelHop: An Enhanced PixelHop Method for Object Classification—0
Tropical cyclone intensity estimations over the Indian ocean using Machine Learning—0
TagRec: Automated Tagging of Questions with Hierarchical Learning TaxonomyCode0
Understanding Cognitive Fatigue from fMRI Scans with Self-supervised Learning—0
Multi-Class Classification of Blood Cells -- End to End Computer Vision based diagnosis case study—0
Multi-Class Classification from Single-Class Data with Confidences—0
Deep Transfer Learning for Brain Magnetic Resonance Image Multi-class Classification—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