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 271–280 of 903 papers

TitleStatusHype
Capsules as viewpoint learners for human pose estimation—0
A Novel Online Real-time Classifier for Multi-label Data Streams—0
A novel online multi-label classifier for high-speed streaming data applications—0
A Fully Memristive Spiking Neural Network with Unsupervised Learning—0
Aanisha@TamilNLP-ACL2022:Abusive Detection in Tamil—0
Achieving Reliable and Fair Skin Lesion Diagnosis via Unsupervised Domain Adaptation—0
Candidates vs. Noises Estimation for Large Multi-Class Classification Problem—0
On the computational complexity of the probabilistic label tree algorithms—0
Understanding and Reducing the Class-Dependent Effects of Data Augmentation with A Two-Player Game Approach—0
Enhancing Multi-Class Classification of Random Forest using Random Vector Functional Neural Network and Oblique Decision Surfaces—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
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1SVM (tficf)Macro F173.9—Unverified
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1Extra TreesF1-Score93.36—Unverified
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1Multi-Model EnsembleMean AUC0.99—Unverified