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 376–400 of 903 papers

TitleStatusHype
"Flux+Mutability": A Conditional Generative Approach to One-Class Classification and Anomaly Detection—0
Wound Severity Classification using Deep Neural Network—0
Few-Shot Transfer Learning to improve Chest X-Ray pathology detection using limited tripletsCode0
The Tree Loss: Improving Generalization with Many Classes—0
Prognostic classification based on random convolutional kernel—0
Deep Learning-Based Intra Mode Derivation for Versatile Video Coding—0
A Novel Approach for detecting Normal, COVID-19 and Pneumonia patient using only binary classifications from chest CT-ScansCode1
Learning curves for the multi-class teacher-student perceptronCode0
Multi Expression Programming for solving classification problems—0
Set-valued prediction in hierarchical classification with constrained representation complexity—0
Automatic Identification and Classification of Bragging in Social Media—0
Diagnosis of COVID-19 using chest X-ray images based on modified DarkCovidNet modelCode0
Multi-channel deep convolutional neural networks for multi-classifying thyroid disease—0
Attention-based Region of Interest (ROI) Detection for Speech Emotion Recognition—0
A Fully Memristive Spiking Neural Network with Unsupervised Learning—0
Counterfactual Explanations for Predictive Business Process Monitoring—0
Self-Training: A Survey—0
Towards Speaker Age Estimation with Label Distribution Learning—0
Personalized Federated Learning with Exact Stochastic Gradient Descent—0
Improving the repeatability of deep learning models with Monte Carlo dropoutCode0
Multi-class granular approximation by means of disjoint and adjacent fuzzy granules—0
Incremental user embedding modeling for personalized text classification—0
FORML: Learning to Reweight Data for Fairness—0
Learning Optimal Topology for Ad-hoc Robot Networks—0
Polyphonic audio event detection: multi-label or multi-class multi-task classification problem?—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
#ModelMetricClaimedVerifiedStatus
1Extra TreesF1-Score93.36—Unverified
#ModelMetricClaimedVerifiedStatus
1Multi-Model EnsembleMean AUC0.99—Unverified