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 281–290 of 903 papers

TitleStatusHype
Error-Correcting Factorization—0
Error-Correcting Output Codes with Ensemble Diversity for Robust Learning in Neural Networks—0
Active Learning for Structured Probabilistic Models With Histogram Approximation—0
A Novel Adaptive Hybrid Focal-Entropy Loss for Enhancing Diabetic Retinopathy Detection Using Convolutional Neural Networks—0
Efficient Transformed Gaussian Processes for Non-Stationary Dependent Multi-class Classification—0
Building an Interpretable Recommender via Loss-Preserving Transformation—0
Bridging Social Media via Distant Supervision—0
Efficient or Powerful? Trade-offs Between Machine Learning and Deep Learning for Mental Illness Detection on Social Media—0
A Non-Intrusive Correction Algorithm for Classification Problems with Corrupted Data—0
Entangled Relations: Leveraging NLI and Meta-analysis to Enhance Biomedical Relation Extraction—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