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 721–730 of 903 papers

TitleStatusHype
Achieving Reliable and Fair Skin Lesion Diagnosis via Unsupervised Domain Adaptation—0
Capsules as viewpoint learners for human pose estimation—0
Cascading Machine Learning to Attack Bitcoin Anonymity—0
Causality-Driven One-Shot Learning for Prostate Cancer Grading from MRI—0
Certified Robustness to Label-Flipping Attacks via Randomized Smoothing—0
Self-Supervision and Multi-Task Learning: Challenges in Fine-Grained COVID-19 Multi-Class Classification from Chest X-rays—0
Characterizing the Optimal 0-1 Loss for Multi-class Classification with a Test-time Attacker—0
Classification based on Topological Data Analysis—0
Classification of Operational Records in Aviation Using Deep Learning Approaches—0
Classification with many classes: challenges and pluses—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