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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 231–240 of 903 papers

TitleStatusHype
Apple Counting using Convolutional Neural Networks—0
Deep Learning Approaches for Blood Disease Diagnosis Across Hematopoietic Lineages—0
Deep Learning-based automated classification of Chinese Speech Sound Disorders—0
Deep Learning-Based Intra Mode Derivation for Versatile Video Coding—0
Automatic Classification of Functional Gait Disorders—0
Deep Multi Label Classification in Affine Subspaces—0
Automatic Detection of Knee Joints and Quantification of Knee Osteoarthritis Severity using Convolutional Neural Networks—0
DeepNAT: Deep Convolutional Neural Network for Segmenting Neuroanatomy—0
Deep reinforced active learning for multi-class image classification—0
Classified as unknown: A novel Bayesian neural network—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
#ModelMetricClaimedVerifiedStatus
1Extra TreesF1-Score93.36—Unverified
#ModelMetricClaimedVerifiedStatus
1Multi-Model EnsembleMean AUC0.99—Unverified