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 51–75 of 903 papers

TitleStatusHype
Clinical Relation Extraction Using Transformer-based ModelsCode1
Co-attention network with label embedding for text classificationCode1
Null It Out: Guarding Protected Attributes by Iterative Nullspace ProjectionCode1
One-step and Two-step Classification for Abusive Language Detection on TwitterCode1
Does your model understand genes? A benchmark of gene properties for biological and text modelsCode1
PANDA: Adapting Pretrained Features for Anomaly Detection and SegmentationCode1
A Deep Neural Network for SSVEP-based Brain-Computer InterfacesCode1
Constrained Optimization to Train Neural Networks on Critical and Under-Represented ClassesCode1
COVID-CXNet: Detecting COVID-19 in Frontal Chest X-ray Images using Deep LearningCode1
Spatio-Temporal EEG Representation Learning on Riemannian Manifold and Euclidean SpaceCode1
Curriculum learning for improved femur fracture classification: scheduling data with prior knowledge and uncertaintyCode1
Self-Supervised Meta-Learning for Few-Shot Natural Language Classification TasksCode1
Detecting Spam Reviews on Vietnamese E-commerce WebsitesCode1
DomURLs_BERT: Pre-trained BERT-based Model for Malicious Domains and URLs Detection and ClassificationCode1
A Practioner's Guide to Evaluating Entity Resolution ResultsCode1
Can multi-label classification networks know what they don’t know?Code1
Efficient Set-Valued Prediction in Multi-Class ClassificationCode1
Emoji Prediction from Twitter Data using Deep Learning ApproachCode1
ArtiFact: A Large-Scale Dataset with Artificial and Factual Images for Generalizable and Robust Synthetic Image DetectionCode1
Entailment as Robust Self-LearnerCode1
Automated detection of COVID-19 cases using deep neural networks with X-ray imagesCode1
Explainable Causal Analysis of Mental Health on Social Media DataCode1
A Novel Approach for detecting Normal, COVID-19 and Pneumonia patient using only binary classifications from chest CT-ScansCode1
A Fully Hyperbolic Neural Model for Hierarchical Multi-Class ClassificationCode1
Dual-Objective Fine-Tuning of BERT for Entity MatchingCode1
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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
#ModelMetricClaimedVerifiedStatus
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