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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 651–660 of 903 papers

TitleStatusHype
A multi-class structured dictionary learning method using discriminant atom selection—0
A Multi-In and Multi-Out Dendritic Neuron Model and its Optimization—0
A multi-label, dual-output deep neural network for automated bug triaging—0
A multi-perspective combined recall and rank framework for Chinese procedure terminology normalization—0
A Multi-Plant Disease Diagnosis Method using Convolutional Neural Network—0
A Multi-Task Self-Normalizing 3D-CNN to Infer Tuberculosis Radiological Manifestations—0
Analysis and classification of heart diseases using heartbeat features and machine learning algorithms—0
Analysis of the Effect of Unexpected Outliers in the Classification of Spectroscopy Data—0
Analysis of Zero Day Attack Detection Using MLP and XAI—0
An Asymptotically Optimal Contextual Bandit Algorithm Using Hierarchical Structures—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