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 471–480 of 903 papers

TitleStatusHype
Prediction and outlier detection in classification problems—0
Predictor-Rejector Multi-Class Abstention: Theoretical Analysis and Algorithms—0
Privacy-Preserving Model and Preprocessing Verification for Machine Learning—0
Probabilistic Classification Vector Machine for Multi-Class Classification—0
Probabilistic Quantum SVM Training on Ising Machine—0
Prognostic classification based on random convolutional kernel—0
Progressive Fashion Attribute Extraction—0
Projection Valued Measure-based Quantum Machine Learning for Multi-Class Classification—0
Provably Consistent Partial-Label Learning—0
Punctuation as Native Language Interference—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