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 601–610 of 903 papers

TitleStatusHype
kNN Classification of Malware Data Dependency Graph Features—0
Label Embedding Trees for Large Multi-Class Tasks—0
Label-free pathological subtyping of non-small cell lung cancer using deep classification and virtual immunohistochemical staining—0
Label Mapping Neural Networks with Response Consolidation for Class Incremental Learning—0
Large Language Models for Multi-Choice Question Classification of Medical Subjects—0
Large Margin Taxonomy Embedding for Document Categorization—0
Large scale classification in deep neural network with Label Mapping—0
Implicit Regularization in Feedback Alignment Learning Mechanisms for Neural Networks—0
Deep Layer-wise Networks Have Closed-Form Weights—0
Learnability with Indirect Supervision Signals—0
Show:102550
← PrevPage 61 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