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 601–625 of 903 papers

TitleStatusHype
Consistent Structured Prediction with Max-Min Margin Markov NetworksCode0
Deep brain state classification of MEG dataCode0
AutoBayes: Automated Bayesian Graph Exploration for Nuisance-Robust Inference—0
Dynamic Sentence Boundary Detection for Simultaneous Translation—0
Improving Low-Resource Named Entity Recognition using Joint Sentence and Token Labeling—0
Probabilistic Classification Vector Machine for Multi-Class Classification—0
Want to Identify, Extract and Normalize Adverse Drug Reactions in Tweets? Use RoBERTa—0
An Integer Linear Programming Framework for Mining Constraints from DataCode0
Deep Layer-wise Networks Have Closed-Form Weights—0
Learnability with Indirect Supervision Signals—0
MixMOOD: A systematic approach to class distribution mismatch in semi-supervised learning using deep dataset dissimilarity measuresCode0
HMIC: Hierarchical Medical Image Classification, A Deep Learning Approach—0
Adaptive Gradient Methods Converge Faster with Over-Parameterization (but you should do a line-search)Code0
Beyond Triplet Loss: Meta Prototypical N-tuple Loss for Person Re-identification—0
Achieving Equalized Odds by Resampling Sensitive AttributesCode0
On the Equivalence between Online and Private Learnability beyond Binary Classification—0
A Framework for Behavioral Biometric Authentication using Deep Metric Learning on Mobile Devices—0
Minimizing Supervision in Multi-label Categorization—0
AutoMSC: Automatic Assignment of Mathematics Subject Classification LabelsCode0
Evolutionary Simplicial Learning as a Generative and Compact Sparse Framework for Classification—0
On Using Transfer Learning For Plant Disease Detection—0
Automated diagnosis of COVID-19 with limited posteroanterior chest X-ray images using fine-tuned deep neural networksCode0
Federated Learning with Only Positive LabelsCode0
Conditional-UNet: A Condition-aware Deep Model for Coherent Human Activity Recognition From Wearables—0
COVID-ResNet: A Deep Learning Framework for Screening of COVID19 from RadiographsCode0
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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
#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