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 201–250 of 903 papers

TitleStatusHype
Lightweight Conditional Model Extrapolation for Streaming Data under Class-Prior ShiftCode0
Llama Guard: LLM-based Input-Output Safeguard for Human-AI ConversationsCode0
Achieving Equalized Odds by Resampling Sensitive AttributesCode0
Lovasz Convolutional NetworksCode0
Machine Learning Methods for Track Classification in the AT-TPCCode0
Generating CCG CategoriesCode0
Learning from Concealed LabelsCode0
FA-Net: A Fuzzy Attention-aided Deep Neural Network for Pneumonia Detection in Chest X-RaysCode0
COVID-ResNet: A Deep Learning Framework for Screening of COVID19 from RadiographsCode0
Attention-based Context Aggregation Network for Monocular Depth EstimationCode0
Federated Learning with Only Positive LabelsCode0
Exponentially Convergent Algorithms for Supervised Matrix FactorizationCode0
Revisiting Classification Perspective on Scene Text RecognitionCode0
Exploring the Uncertainty Properties of Neural Networks' Implicit Priors in the Infinite-Width LimitCode0
Multimodal Speech Emotion Recognition and Ambiguity ResolutionCode0
Extrapolating Expected Accuracies for Large Multi-Class ProblemsCode0
Label Distributionally Robust Losses for Multi-class Classification: Consistency, Robustness and AdaptivityCode0
Network Representation Learning with Rich Text InformationCode0
Few-Shot Transfer Learning to improve Chest X-Ray pathology detection using limited tripletsCode0
Neuro-Argumentative Learning with Case-Based ReasoningCode0
DCAST: Diverse Class-Aware Self-Training Mitigates Selection Bias for Fairer LearningCode0
Noise-Free Explanation for Driving Action PredictionCode0
Explaining Convolutional Neural Networks using Softmax Gradient Layer-wise Relevance PropagationCode0
Active Learning from Positive and Unlabeled DataCode0
Financial Data Analysis with Robust Federated Logistic RegressionCode0
Deep attention-based classification network for robust depth predictionCode0
Automated diagnosis of COVID-19 with limited posteroanterior chest X-ray images using fine-tuned deep neural networksCode0
A Novel Multi-Stage Approach for Hierarchical Intrusion DetectionCode0
Deep brain state classification of MEG dataCode0
Optimal-margin evolutionary classifierCode0
Every Untrue Label is Untrue in its Own Way: Controlling Error Type with the Log Bilinear LossCode0
CAMRI Loss: Improving Recall of a Specific Class without Sacrificing AccuracyCode0
Pairwise Margin Maximization for Deep Neural NetworksCode0
Calibration tests in multi-class classification: A unifying frameworkCode0
Deep localization of protein structures in fluorescence microscopy imagesCode0
Analysis of French Phonetic Idiosyncrasies for Accent RecognitionCode0
Deep N-ary Error Correcting Output CodesCode0
Predicting delays in Indian lower courts using AutoML and Decision ForestsCode0
Calibration tests beyond classificationCode0
Calibrated simplex-mapping classificationCode0
Evaluating ML-Based Anomaly Detection Across Datasets of Varied Integrity: A Case StudyCode0
Additive interaction modelling using I-priorsCode0
A Full Probabilistic Model for Yes/No Type Crowdsourcing in Multi-Class ClassificationCode0
AutoMSC: Automatic Assignment of Mathematics Subject Classification LabelsCode0
Evaluating approaches for supervised semantic labelingCode0
Safe reinforcement learning in uncertain contextsCode0
Explainable AI for Comparative Analysis of Intrusion Detection ModelsCode0
Self-supervised Mean Teacher for Semi-supervised Chest X-ray ClassificationCode0
Batch Selection for Multi-Label Classification Guided by Uncertainty and Dynamic Label CorrelationsCode0
Breast Tumor Classification Using EfficientNet Deep Learning ModelCode0
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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