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 31–40 of 903 papers

TitleStatusHype
Quantum Complex-Valued Self-Attention Model—0
Automated diagnosis of lung diseases using vision transformer: a comparative study on chest x-ray classification—0
Probabilistic Quantum SVM Training on Ising Machine—0
Multi-output Classification for Compound Fault Diagnosis in Motor under Partially Labeled Target Domain—0
Double-Stage Feature-Level Clustering-Based Mixture of Experts Framework—0
KréyoLID From Language Identification Towards Language MiningCode0
Efficient or Powerful? Trade-offs Between Machine Learning and Deep Learning for Mental Illness Detection on Social Media—0
Predicting Cascading Failures in Power Systems using Machine Learning—0
When Unsupervised Domain Adaptation meets One-class Anomaly Detection: Addressing the Two-fold Unsupervised Curse by Leveraging Anomaly Scarcity—0
Armijo Line-search Can Make (Stochastic) Gradient Descent Provably Faster—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