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 26–50 of 903 papers

TitleStatusHype
HMIL: Hierarchical Multi-Instance Learning for Fine-Grained Whole Slide Image ClassificationCode1
Inductive Conformal Prediction: A Straightforward Introduction with Examples in PythonCode1
A Deep Neural Network for SSVEP-based Brain-Computer InterfacesCode1
Invariant-Feature Subspace Recovery: A New Class of Provable Domain Generalization AlgorithmsCode1
IoTDevID: A Behavior-Based Device Identification Method for the IoTCode1
Co-attention network with label embedding for text classificationCode1
Can multi-label classification networks know what they don’t know?Code1
Can NLI Provide Proper Indirect Supervision for Low-resource Biomedical Relation Extraction?Code1
Constrained Optimization to Train Neural Networks on Critical and Under-Represented ClassesCode1
Automated detection of COVID-19 cases using deep neural networks with X-ray imagesCode1
A Fully Hyperbolic Neural Model for Hierarchical Multi-Class ClassificationCode1
CIGAN: A Python Package for Handling Class Imbalance using Generative Adversarial NetworksCode1
Clinical Relation Extraction Using Transformer-based ModelsCode1
COVID-CXNet: Detecting COVID-19 in Frontal Chest X-ray Images using Deep LearningCode1
Curriculum learning for improved femur fracture classification: scheduling data with prior knowledge and uncertaintyCode1
Does your model understand genes? A benchmark of gene properties for biological and text modelsCode1
DomURLs_BERT: Pre-trained BERT-based Model for Malicious Domains and URLs Detection and ClassificationCode1
Emoji Prediction from Twitter Data using Deep Learning ApproachCode1
Enabling Mixed Effects Neural Networks for Diverse, Clustered Data Using Monte Carlo MethodsCode1
Enumerating the k-fold configurations in multi-class classification problemsCode1
Event-Event Relation Extraction using Probabilistic Box EmbeddingCode1
ArtiFact: A Large-Scale Dataset with Artificial and Factual Images for Generalizable and Robust Synthetic Image DetectionCode1
Findings of the The RuATD Shared Task 2022 on Artificial Text Detection in RussianCode1
BAdaCost: Multi-class Boosting with CostsCode1
A Novel Approach for detecting Normal, COVID-19 and Pneumonia patient using only binary classifications from chest CT-ScansCode1
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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