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 91–100 of 903 papers

TitleStatusHype
DCAST: Diverse Class-Aware Self-Training Mitigates Selection Bias for Fairer LearningCode0
More Consideration for the PerceptronCode0
Enhancing Personalized Recipe Recommendation Through Multi-Class Classification—0
Self-supervised Multimodal Speech Representations for the Assessment of Schizophrenia Symptoms—0
Detection Made Easy: Potentials of Large Language Models for Solidity Vulnerabilities—0
DomURLs_BERT: Pre-trained BERT-based Model for Malicious Domains and URLs Detection and ClassificationCode1
TabKANet: Tabular Data Modeling with Kolmogorov-Arnold Network and TransformerCode1
Constrained Multi-Layer Contrastive Learning for Implicit Discourse Relationship Recognition—0
Multi-Output Distributional Fairness via Post-Processing—0
XG-NID: Dual-Modality Network Intrusion Detection using a Heterogeneous Graph Neural Network and Large Language ModelCode1
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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