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 151–175 of 903 papers

TitleStatusHype
Generalized Consistent Multi-Class Classification with Rejection to be Compatible with Arbitrary LossesCode0
GenSVM: A Generalized Multiclass Support Vector MachineCode0
HemaGraph: Breaking Barriers in Hematologic Single Cell Classification with Graph AttentionCode0
A Generalized Unbiased Risk Estimator for Learning with Augmented ClassesCode0
Coarse and Fine-Grained Hostility Detection in Hindi Posts using Fine Tuned Multilingual EmbeddingsCode0
Few-Shot Transfer Learning to improve Chest X-Ray pathology detection using limited tripletsCode0
A generalized framework to predict continuous scores from medical ordinal labelsCode0
Financial Data Analysis with Robust Federated Logistic RegressionCode0
FlowCyt: A Comparative Study of Deep Learning Approaches for Multi-Class Classification in Flow Cytometry BenchmarkingCode0
Extrapolating Expected Accuracies for Large Multi-Class ProblemsCode0
AppealCase: A Dataset and Benchmark for Civil Case Appeal ScenariosCode0
Explaining Convolutional Neural Networks using Softmax Gradient Layer-wise Relevance PropagationCode0
FA-Net: A Fuzzy Attention-aided Deep Neural Network for Pneumonia Detection in Chest X-RaysCode0
Every Untrue Label is Untrue in its Own Way: Controlling Error Type with the Log Bilinear LossCode0
Evaluating approaches for supervised semantic labelingCode0
Exponentially Convergent Algorithms for Supervised Matrix FactorizationCode0
Application of Quantum Pre-Processing Filter for Binary Image Classification with Small SamplesCode0
Evaluating ML-Based Anomaly Detection Across Datasets of Varied Integrity: A Case StudyCode0
Application of SsVGMM to medical data-classification with novelty detectionCode0
Federated Learning with Only Positive LabelsCode0
Explainable AI for Comparative Analysis of Intrusion Detection ModelsCode0
Food Classification with Convolutional Neural Networks and Multi-Class Linear Discernment AnalysisCode0
Emulating Spatio-Temporal Realizations of Three-Dimensional Isotropic Turbulence via Deep Sequence Learning ModelsCode0
Adaptive Gradient Methods Converge Faster with Over-Parameterization (but you should do a line-search)Code0
Efficient Machine Learning Ensemble Methods for Detecting Gravitational Wave Glitches in LIGO Time SeriesCode0
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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