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 131–140 of 903 papers

TitleStatusHype
kNN Classification of Malware Data Dependency Graph Features—0
Annotation Guidelines-Based Knowledge Augmentation: Towards Enhancing Large Language Models for Educational Text Classification—0
Understanding and Reducing the Class-Dependent Effects of Data Augmentation with A Two-Player Game Approach—0
Entangled Relations: Leveraging NLI and Meta-analysis to Enhance Biomedical Relation Extraction—0
Masked Language Modeling Becomes Conditional Density Estimation for Tabular Data Synthesis—0
Sheaf HyperNetworks for Personalized Federated Learning—0
Domain Adaptation with Cauchy-Schwarz DivergenceCode0
Injecting Hierarchical Biological Priors into Graph Neural Networks for Flow Cytometry PredictionCode0
Neural Collapse versus Low-rank Bias: Is Deep Neural Collapse Really Optimal?—0
Inverse Design of Metal-Organic Frameworks Using Quantum Natural Language ProcessingCode0
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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