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 211–220 of 903 papers

TitleStatusHype
Sequential Binary Classification for Intrusion Detection—0
Novel Approach to Intrusion Detection: Introducing GAN-MSCNN-BILSTM with LIME Predictions—0
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
Sheaf HyperNetworks for Personalized Federated Learning—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
Domain Adaptation with Cauchy-Schwarz DivergenceCode0
Injecting Hierarchical Biological Priors into Graph Neural Networks for Flow Cytometry PredictionCode0
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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