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 426–450 of 903 papers

TitleStatusHype
Prognostic classification based on random convolutional kernel—0
Deep Learning-Based Intra Mode Derivation for Versatile Video Coding—0
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
Self-Supervision and Multi-Task Learning: Challenges in Fine-Grained COVID-19 Multi-Class Classification from Chest X-rays—0
The SAMME.C2 algorithm for severely imbalanced multi-class classification—0
Label Distributionally Robust Losses for Multi-class Classification: Consistency, Robustness and AdaptivityCode0
Scalable Batch-Mode Deep Bayesian Active Learning via Equivalence Class AnnealingCode0
Contrastive Learning for Fair Representations—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