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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 151–160 of 903 papers

TitleStatusHype
Multi-Class Quantum Convolutional Neural Networks—0
Hyper Evidential Deep Learning to Quantify Composite Classification UncertaintyCode1
Exploring Contrastive Learning for Long-Tailed Multi-Label Text Classification—0
Unknown Prompt, the only Lacuna: Unveiling CLIP's Potential for Open Domain GeneralizationCode1
Top-k Classification and Cardinality-Aware Prediction—0
Cross-System Categorization of Abnormal Traces in Microservice-Based Systems via Meta-Learning—0
Large Language Models for Multi-Choice Question Classification of Medical Subjects—0
Hierarchical Classification for Intrusion Detection System: Effective Design and Empirical Analysis—0
FingerNet: EEG Decoding of A Fine Motor Imagery with Finger-tapping Task Based on A Deep Neural Network—0
Neural Network Learning and Quantum Gravity—0
Show:102550
← PrevPage 16 of 91Next →
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
#ModelMetricClaimedVerifiedStatus
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