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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 671–680 of 903 papers

TitleStatusHype
A novel online multi-label classifier for high-speed streaming data applications—0
A Novel Online Real-time Classifier for Multi-label Data Streams—0
A Novel Progressive Learning Technique for Multi-class Classification—0
Antifragility Predicts the Robustness and Evolvability of Biological Networks through Multi-class Classification with a Convolutional Neural Network—0
An Unsupervised Domain-Independent Framework for Automated Detection of Persuasion Tactics in Text—0
A One-Sided Classification Toolkit with Applications in the Analysis of Spectroscopy Data—0
Apple Counting using Convolutional Neural Networks—0
A pragmatic approach to multi-class classification—0
A priori estimates for classification problems using neural networks—0
A procedure for assessing of machine health index data prediction quality—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