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 151–200 of 903 papers

TitleStatusHype
Information-theoretic Classification Accuracy: A Criterion that Guides Data-driven Combination of Ambiguous Outcome Labels in Multi-class ClassificationCode0
Interval Abstractions for Robust Counterfactual ExplanationsCode0
Improving Bias Mitigation through Bias Experts in Natural Language UnderstandingCode0
A Quest for Structure: Jointly Learning the Graph Structure and Semi-Supervised ClassificationCode0
Improving Generalizability of Kolmogorov-Arnold Networks via Error-Correcting Output CodesCode0
Combating Hostility: Covid-19 Fake News and Hostile Post Detection in Social MediaCode0
HemaGraph: Breaking Barriers in Hematologic Single Cell Classification with Graph AttentionCode0
Improving the repeatability of deep learning models with Monte Carlo dropoutCode0
Fuzzy granular approximation classifierCode0
Coarse and Fine-Grained Hostility Detection in Hindi Posts using Fine Tuned Multilingual EmbeddingsCode0
Generalized Consistent Multi-Class Classification with Rejection to be Compatible with Arbitrary LossesCode0
Food Classification with Convolutional Neural Networks and Multi-Class Linear Discernment AnalysisCode0
Application of Quantum Pre-Processing Filter for Binary Image Classification with Small SamplesCode0
A generalized framework to predict continuous scores from medical ordinal labelsCode0
Fusing Structure and Content via Non-negative Matrix Factorization for Embedding Information NetworksCode0
Generating CCG CategoriesCode0
Few-Shot Transfer Learning to improve Chest X-Ray pathology detection using limited tripletsCode0
Federated Learning with Only Positive LabelsCode0
Application of SsVGMM to medical data-classification with novelty detectionCode0
FlowCyt: A Comparative Study of Deep Learning Approaches for Multi-Class Classification in Flow Cytometry BenchmarkingCode0
Financial Data Analysis with Robust Federated Logistic RegressionCode0
FA-Net: A Fuzzy Attention-aided Deep Neural Network for Pneumonia Detection in Chest X-RaysCode0
A Generalized Unbiased Risk Estimator for Learning with Augmented ClassesCode0
Adaptive Gradient Methods Converge Faster with Over-Parameterization (but you should do a line-search)Code0
AppealCase: A Dataset and Benchmark for Civil Case Appeal ScenariosCode0
Exploring the Uncertainty Properties of Neural Networks' Implicit Priors in the Infinite-Width LimitCode0
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
HSD Shared Task in VLSP Campaign 2019:Hate Speech Detection for Social GoodCode0
Aggressive Sampling for Multi-class to Binary Reduction with Applications to Text ClassificationCode0
Imbalance Learning for Variable Star ClassificationCode0
Adaptive Sampled Softmax with Inverted Multi-Index: Methods, Theory and ApplicationsCode0
GenSVM: A Generalized Multiclass Support Vector MachineCode0
Inverse-Category-Frequency based supervised term weighting scheme for text categorizationCode0
InClass Nets: Independent Classifier Networks for Nonparametric Estimation of Conditional Independence Mixture Models and Unsupervised ClassificationCode0
Evaluating approaches for supervised semantic labelingCode0
Conclusive Local Interpretation Rules for Random ForestsCode0
Evaluating ML-Based Anomaly Detection Across Datasets of Varied Integrity: A Case StudyCode0
Condensed Gradient BoostingCode0
Ensembling Uncertainty Measures to Improve Safety of Black-Box ClassifiersCode0
Enhanced Network Embedding with Text InformationCode0
Conformal inference is (almost) free for neural networks trained with early stoppingCode0
Conformalized Semi-supervised Random Forest for Classification and Abnormality DetectionCode0
A Semantic Loss Function for Deep Learning with Symbolic KnowledgeCode0
Consistent Structured Prediction with Max-Min Margin Markov NetworksCode0
A matter of attitude: Focusing on positive and active gradients to boost saliency mapsCode0
Emulating Spatio-Temporal Realizations of Three-Dimensional Isotropic Turbulence via Deep Sequence Learning ModelsCode0
Active Learning from Positive and Unlabeled DataCode0
Every Untrue Label is Untrue in its Own Way: Controlling Error Type with the Log Bilinear LossCode0
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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