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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 531–540 of 903 papers

TitleStatusHype
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
Energy-based Out-of-distribution Detection for Multi-label Classification—0
Uncertainty Calibration Error: A New Metric for Multi-Class Classification—0
Convolutional Neural Networks in Multi-Class Classification of Medical Data—0
Multidimensional Uncertainty-Aware Evidential Neural NetworksCode1
Explainable Multi-class Classification of Medical Data—0
Light-Weight 1-D Convolutional Neural Network Architecture for Mental Task Identification and Classification Based on Single-Channel EEG—0
Robustness of Accuracy Metric and its Inspirations in Learning with Noisy LabelsCode1
Food Classification with Convolutional Neural Networks and Multi-Class Linear Discernment AnalysisCode0
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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