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 326–350 of 903 papers

TitleStatusHype
Explainable Multi-class Classification of Medical Data—0
Explainable Multi-class Classification of the CAMH COVID-19 Mental Health Data—0
Classification with many classes: challenges and pluses—0
Explicit Facial Expression Transfer via Fine-Grained Representations—0
FLAG: Fast Label-Adaptive Aggregation for Multi-label Classification in Federated Learning—0
A Framework for Behavioral Biometric Authentication using Deep Metric Learning on Mobile Devices—0
Exploring Contrastive Learning for Long-Tailed Multi-Label Text Classification—0
Exploring Highly Quantised Neural Networks for Intrusion Detection in Automotive CAN—0
Exploring the Uncertainty Properties of Neural Networks’ Implicit Priors in the Infinite-Width Limit—0
Anomaly Detection using Ensemble Classification and Evidence Theory—0
Class-Imbalanced Complementary-Label Learning via Weighted Loss—0
Flat and Nested Negation and Uncertainty Detection with PubMed BERT—0
Eye Disease Classification Using Deep Learning Techniques—0
Factorizable Joint Shift in Multinomial Classification—0
Faint Features Tell: Automatic Vertebrae Fracture Screening Assisted by Contrastive Learning—0
CO2 Forest: Improved Random Forest by Continuous Optimization of Oblique Splits—0
Breaking the Token Barrier: Chunking and Convolution for Efficient Long Text Classification with BERT—0
An Infinite-Feature Extension for Bayesian ReLU Nets That Fixes Their Asymptotic Overconfidence—0
"Flux+Mutability": A Conditional Generative Approach to One-Class Classification and Anomaly Detection—0
Effective Metaheuristic Based Classifiers for Multiclass Intrusion Detection—0
FD-LLM: Large Language Model for Fault Diagnosis of Machines—0
Feature-aware conditional GAN for category text generation—0
Feature Incay for Representation Regularization—0
Cognitive Radar Antenna Selection via Deep Learning—0
Effective Intrusion Detection for UAV Communications using Autoencoder-based Feature Extraction and Machine Learning Approach—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
#ModelMetricClaimedVerifiedStatus
1SVM (tficf)Macro F173.9—Unverified
#ModelMetricClaimedVerifiedStatus
1Extra TreesF1-Score93.36—Unverified
#ModelMetricClaimedVerifiedStatus
1Multi-Model EnsembleMean AUC0.99—Unverified