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 101–125 of 903 papers

TitleStatusHype
Federated Learning with Only Positive LabelsCode0
Few-Shot Transfer Learning to improve Chest X-Ray pathology detection using limited tripletsCode0
Fusing Structure and Content via Non-negative Matrix Factorization for Embedding Information NetworksCode0
Exponentially Convergent Algorithms for Supervised Matrix FactorizationCode0
Explaining Convolutional Neural Networks using Softmax Gradient Layer-wise Relevance PropagationCode0
Extrapolating Expected Accuracies for Large Multi-Class ProblemsCode0
Explainable AI for Comparative Analysis of Intrusion Detection ModelsCode0
Auto deep learning for bioacoustic signalsCode0
3DMASC: Accessible, explainable 3D point clouds classification. Application to Bi-spectral Topo-bathymetric lidar dataCode0
FA-Net: A Fuzzy Attention-aided Deep Neural Network for Pneumonia Detection in Chest X-RaysCode0
Fuzzy granular approximation classifierCode0
Evaluating approaches for supervised semantic labelingCode0
Automated diagnosis of COVID-19 with limited posteroanterior chest X-ray images using fine-tuned deep neural networksCode0
Label Distributionally Robust Losses for Multi-class Classification: Consistency, Robustness and AdaptivityCode0
Enhanced Network Embedding with Text InformationCode0
Ensembling Uncertainty Measures to Improve Safety of Black-Box ClassifiersCode0
Analysis of French Phonetic Idiosyncrasies for Accent RecognitionCode0
Exploring the Uncertainty Properties of Neural Networks' Implicit Priors in the Infinite-Width LimitCode0
Evaluating ML-Based Anomaly Detection Across Datasets of Varied Integrity: A Case StudyCode0
Efficient Robust Optimal Transport with Application to Multi-Label ClassificationCode0
AutoMSC: Automatic Assignment of Mathematics Subject Classification LabelsCode0
Emulating Spatio-Temporal Realizations of Three-Dimensional Isotropic Turbulence via Deep Sequence Learning ModelsCode0
Attention-based Context Aggregation Network for Monocular Depth EstimationCode0
Batch Selection for Multi-Label Classification Guided by Uncertainty and Dynamic Label CorrelationsCode0
A Topological Data Analysis Based ClassifierCode0
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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
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1Extra TreesF1-Score93.36—Unverified
#ModelMetricClaimedVerifiedStatus
1Multi-Model EnsembleMean AUC0.99—Unverified