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 276–300 of 903 papers

TitleStatusHype
Characterizing the Optimal 0-1 Loss for Multi-class Classification with a Test-time Attacker—0
Occupant's Behavior and Emotion Based Indoor Environment's Illumination Regulation—0
Optimal Transport for Change Detection on LiDAR Point CloudsCode0
Capsules as viewpoint learners for human pose estimation—0
Cut your Losses with Squentropy—0
1st Place Solution for PSG competition with ECCV'22 SenseHuman WorkshopCode2
Conformalized Semi-supervised Random Forest for Classification and Abnormality DetectionCode0
Classified as unknown: A novel Bayesian neural network—0
Towards Adversarial Realism and Robust Learning for IoT Intrusion Detection and Classification—0
Conformal inference is (almost) free for neural networks trained with early stoppingCode0
Increasing Fairness via Combination with Learning Guarantees—0
WDC Products: A Multi-Dimensional Entity Matching BenchmarkCode1
ComplAI: Theory of A Unified Framework for Multi-factor Assessment of Black-Box Supervised Machine Learning Models—0
Problem-Dependent Power of Quantum Neural Networks on Multi-Class Classification—0
Anomaly Detection using Ensemble Classification and Evidence Theory—0
Can NLI Provide Proper Indirect Supervision for Low-resource Biomedical Relation Extraction?Code1
Query Your Model with Definitions in FrameNet: An Effective Method for Frame Semantic Role LabelingCode1
YolOOD: Utilizing Object Detection Concepts for Multi-Label Out-of-Distribution DetectionCode1
Learning Disentangled Label Representations for Multi-label Classification—0
Semi-supervised binary classification with latent distance learning—0
X-PuDu at SemEval-2022 Task 7: A Replaced Token Detection Task Pre-trained Model with Pattern-aware Ensembling for Identifying Plausible Clarifications—0
Condensed Gradient BoostingCode0
A Masked Face Classification Benchmark on Low-Resolution Surveillance ImagesCode0
Unsupervised Face Recognition using Unlabeled Synthetic DataCode1
Detecting Disengagement in Virtual Learning as an Anomaly using Temporal Convolutional Network Autoencoder—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
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1SVM (tficf)Macro F173.9—Unverified
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1Extra TreesF1-Score93.36—Unverified
#ModelMetricClaimedVerifiedStatus
1Multi-Model EnsembleMean AUC0.99—Unverified