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 771–780 of 903 papers

TitleStatusHype
Data-Driven Fault Diagnosis Analysis and Open-Set Classification of Time-Series Data—0
Data-driven root-cause analysis for distributed system anomalies—0
DCSVM: Fast Multi-class Classification using Support Vector Machines—0
Decentralized Online Learning with Kernels—0
Decision-forest voting scheme for classification of rare classes in network intrusion detection—0
Deep Attention Model for Triage of Emergency Department Patients—0
Deep Auto-Set: A Deep Auto-Encoder-Set Network for Activity Recognition Using Wearables—0
Deep Convolutional Neural Network based Classification of Alzheimer's Disease using MRI data—0
Deep Learning Approaches for Blood Disease Diagnosis Across Hematopoietic Lineages—0
Deep Learning-based automated classification of Chinese Speech Sound Disorders—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