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 551–575 of 903 papers

TitleStatusHype
Efficient Robust Optimal Transport with Application to Multi-Label ClassificationCode0
Learning Patterns in Imaginary Vowels for an Intelligent Brain Computer Interface (BCI) Design—0
Exploring the Uncertainty Properties of Neural Networks' Implicit Priors in the Infinite-Width Limit—0
Enhancing the Identification of Cyberbullying through Participant Roles—0
PANDA: Adapting Pretrained Features for Anomaly Detection and SegmentationCode1
Zero-shot Active Learning with Topological Clustering for Multiclass Classification—0
Artificial intelligence supported anemia control system (AISACS) to prevent anemia in maintenance hemodialysis patients—0
An Infinite-Feature Extension for Bayesian ReLU Nets That Fixes Their Asymptotic Overconfidence—0
A Fully Hyperbolic Neural Model for Hierarchical Multi-Class ClassificationCode1
Learning by Minimizing the Sum of Ranked RangeCode0
An ensemble of Density based Geometric One-Class Classifier and Genetic Algorithm—0
Fixing Asymptotic Uncertainty of Bayesian Neural Networks with Infinite ReLU Features—0
A priori estimates for classification problems using neural networks—0
Self-Weighted Robust LDA for Multiclass Classification with Edge Classes—0
Deep N-ary Error Correcting Output CodesCode0
Self-Supervised Meta-Learning for Few-Shot Natural Language Classification TasksCode1
Solomon at SemEval-2020 Task 11: Ensemble Architecture for Fine-Tuned Propaganda Detection in News Articles—0
Multimodal Depression Severity Prediction from medical bio-markers using Machine Learning Tools and Technologies—0
Data-Driven Fault Diagnosis Analysis and Open-Set Classification of Time-Series Data—0
InClass Nets: Independent Classifier Networks for Nonparametric Estimation of Conditional Independence Mixture Models and Unsupervised ClassificationCode0
Simulation-supervised deep learning for analysing organelles states and behaviour in living cells—0
Neural Neighborhood Encoding for Classification—0
Spatio-Temporal EEG Representation Learning on Riemannian Manifold and Euclidean SpaceCode1
Metrics for Multi-Class Classification: an Overview—0
Residual Generation Using Physically-Based Grey-Box Recurrent Neural Networks For Engine Fault Diagnosis—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