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 591–600 of 903 papers

TitleStatusHype
TRk-CNN: Transferable Ranking-CNN for image classification of glaucoma, glaucoma suspect, and normal eyes—0
Tropical cyclone intensity estimations over the Indian ocean using Machine Learning—0
TunnElQNN: A Hybrid Quantum-classical Neural Network for Efficient Learning—0
Tweet Acts: A Speech Act Classifier for Twitter—0
UB Health Miners@SMM4H’22: Exploring Pre-processing Techniques To Classify Tweets Using Transformer Based Pipelines.—0
A New Periocular Dataset Collected by Mobile Devices in Unconstrained Scenarios—0
Uncertainty-aware abstention in medical diagnosis based on medical texts—0
Uncertainty Calibration Error: A New Metric for Multi-Class Classification—0
Understanding Cognitive Fatigue from fMRI Scans with Self-supervised Learning—0
Understanding Self-Distillation and Partial Label Learning in Multi-Class Classification with Label Noise—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
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1Multi-Model EnsembleMean AUC0.99—Unverified