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 331–340 of 903 papers

TitleStatusHype
AutoBayes: Automated Bayesian Graph Exploration for Nuisance-Robust Inference—0
Data-driven root-cause analysis for distributed system anomalies—0
Data-Driven Fault Diagnosis Analysis and Open-Set Classification of Time-Series Data—0
A Universal Growth Rate for Learning with Smooth Surrogate Losses—0
A Multi-Plant Disease Diagnosis Method using Convolutional Neural Network—0
Data-dependent Generalization Bounds for Multi-class Classification—0
CyberLearning: Effectiveness Analysis of Machine Learning Security Modeling to Detect Cyber-Anomalies and Multi-Attacks—0
Cut your Losses with Squentropy—0
Curriculum Learning for Speech Emotion Recognition from Crowdsourced Labels—0
Respiratory Disease Classification and Biometric Analysis Using Biosignals from Digital Stethoscopes—0
Show:102550
← PrevPage 34 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