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 311–320 of 903 papers

TitleStatusHype
Achieving Reliable and Fair Skin Lesion Diagnosis via Unsupervised Domain Adaptation—0
CMA-ES for Post Hoc Ensembling in AutoML: A Great Success and Salvageable Failure—0
Improving Primate Sounds Classification using Binary Presorting for Deep Learning—0
Is the Volume of a Credal Set a Good Measure for Epistemic Uncertainty?—0
Kalman Filter for Online Classification of Non-Stationary Data—0
A Generalized Unbiased Risk Estimator for Learning with Augmented ClassesCode0
Implicit Regularization in Feedback Alignment Learning Mechanisms for Neural Networks—0
A Stutter Seldom Comes Alone -- Cross-Corpus Stuttering Detection as a Multi-label Problem—0
A generalized framework to predict continuous scores from medical ordinal labelsCode0
Learning across Data Owners with Joint Differential Privacy—0
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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
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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