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 551–560 of 903 papers

TitleStatusHype
An Experimental Analysis of Attack Classification Using Machine Learning in IoT Networks—0
Combating Hostility: Covid-19 Fake News and Hostile Post Detection in Social MediaCode0
One-Class Classification: A Survey—0
Deep Convolutional Neural Network based Classification of Alzheimer's Disease using MRI data—0
One vs Previous and Similar Classes Learning -- A Comparative Study—0
Uncertainty Calibration Error: A New Metric for Multi-Class Classification—0
Energy-based Out-of-distribution Detection for Multi-label Classification—0
Maximum Categorical Cross Entropy (MCCE): A noise-robust alternative loss function to mitigate racial bias in Convolutional Neural Networks (CNNs) by reducing overfitting—0
Exploring the Uncertainty Properties of Neural Networks’ Implicit Priors in the Infinite-Width Limit—0
Ensemble-based Adversarial Defense Using Diversified Distance Mapping—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