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 321–330 of 903 papers

TitleStatusHype
Automated diagnosis of lung diseases using vision transformer: a comparative study on chest x-ray classification—0
Deep Auto-Set: A Deep Auto-Encoder-Set Network for Activity Recognition Using Wearables—0
Deep Attention Model for Triage of Emergency Department Patients—0
Analysis and classification of heart diseases using heartbeat features and machine learning algorithms—0
Decision-forest voting scheme for classification of rare classes in network intrusion detection—0
Decentralized Online Learning with Kernels—0
A Multi-Task Self-Normalizing 3D-CNN to Infer Tuberculosis Radiological Manifestations—0
A Deep Generative Approach to Native Language Identification—0
A Comprehensive Evaluation of Large Language Models on Mental Illnesses in Arabic Context—0
DCSVM: Fast Multi-class Classification using Support Vector Machines—0
Show:102550
← PrevPage 33 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