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 76–100 of 903 papers

TitleStatusHype
Event-Event Relation Extraction using Probabilistic Box EmbeddingCode1
Explainable Causal Analysis of Mental Health on Social Media DataCode1
Exploiting Class Activation Value for Partial-Label LearningCode1
GP-Tree: A Gaussian Process Classifier for Few-Shot Incremental LearningCode1
GraphHop: An Enhanced Label Propagation Method for Node ClassificationCode1
Can multi-label classification networks know what they don't know?Code1
Hyper Evidential Deep Learning to Quantify Composite Classification UncertaintyCode1
Automated detection of COVID-19 cases using deep neural networks with X-ray imagesCode1
Integrated Multi-omics Analysis Using Variational Autoencoders: Application to Pan-cancer ClassificationCode1
A Practioner's Guide to Evaluating Entity Resolution ResultsCode1
IoTDevID: A Behavior-Based Device Identification Method for the IoTCode1
One-Class Risk Estimation for One-Class Hyperspectral Image ClassificationCode1
Learning with Complementary Labels Revisited: The Selected-Completely-at-Random Setting Is More PracticalCode1
Clinical Relation Extraction Using Transformer-based ModelsCode1
Multi-class Gaussian Process Classification with Noisy InputsCode1
Multimodal Attention-based Deep Learning for Alzheimer's Disease DiagnosisCode1
MVMTnet: A Multi-variate Multi-modal Transformer for Multi-class Classification of Cardiac Irregularities Using ECG Waveforms and Clinical NotesCode1
One-step and Two-step Classification for Abusive Language Detection on TwitterCode1
ArtiFact: A Large-Scale Dataset with Artificial and Factual Images for Generalizable and Robust Synthetic Image DetectionCode1
DomURLs_BERT: Pre-trained BERT-based Model for Malicious Domains and URLs Detection and ClassificationCode1
PMLB v1.0: An open source dataset collection for benchmarking machine learning methodsCode1
An Effective Approach for Multi-label Classification with Missing Labels—0
An Attention-based Long Short-Term Memory Framework for Detection of Bitcoin Scams—0
Advancing Efficient Brain Tumor Multi-Class Classification -- New Insights from the Vision Mamba Model in Transfer Learning—0
An Asymptotically Optimal Contextual Bandit Algorithm Using Hierarchical Structures—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
#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