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 51–60 of 903 papers

TitleStatusHype
Privacy-Preserving Model and Preprocessing Verification for Machine Learning—0
A Comprehensive Evaluation of Large Language Models on Mental Illnesses in Arabic Context—0
Classification of Operational Records in Aviation Using Deep Learning Approaches—0
Injecting Explainability and Lightweight Design into Weakly Supervised Video Anomaly Detection Systems—0
An Improved Fault Diagnosis Strategy for Induction Motors Using Weighted Probability Ensemble Deep Learning—0
Towards Macro-AUC oriented Imbalanced Multi-Label Continual LearningCode0
Batch Selection for Multi-Label Classification Guided by Uncertainty and Dynamic Label CorrelationsCode0
When Can Memorization Improve Fairness?—0
Embeddings are all you need! Achieving High Performance Medical Image Classification through Training-Free Embedding Analysis—0
Diagnosis and Severity Assessment of Ulcerative Colitis using Self Supervised Learning—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