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 581–590 of 903 papers

Title	Date	Tasks	Status
An ensemble of Density based Geometric One-Class Classifier and Genetic Algorithm	Oct 2, 2020	ClassificationGeneral Classification	—Unverified
A priori estimates for classification problems using neural networks	Sep 28, 2020	ClassificationGeneral Classification	—Unverified
Fixing Asymptotic Uncertainty of Bayesian Neural Networks with Infinite ReLU Features	Sep 28, 2020	Multi-class Classification	—Unverified
Self-Weighted Robust LDA for Multiclass Classification with Edge Classes	Sep 24, 2020	ClassificationComputational Efficiency	—Unverified
Deep N-ary Error Correcting Output Codes	Sep 22, 2020	ClassificationEnsemble Learning	CodeCode Available
Solomon at SemEval-2020 Task 11: Ensemble Architecture for Fine-Tuned Propaganda Detection in News Articles	Sep 16, 2020	ArticlesClassification	—Unverified
Multimodal Depression Severity Prediction from medical bio-markers using Machine Learning Tools and Technologies	Sep 11, 2020	BIG-bench Machine LearningDecision Making	—Unverified
Data-Driven Fault Diagnosis Analysis and Open-Set Classification of Time-Series Data	Sep 10, 2020	ClassificationFault Diagnosis	—Unverified
InClass Nets: Independent Classifier Networks for Nonparametric Estimation of Conditional Independence Mixture Models and Unsupervised Classification	Aug 31, 2020	BIG-bench Machine LearningMulti-class Classification	CodeCode Available
Simulation-supervised deep learning for analysing organelles states and behaviour in living cells	Aug 26, 2020	Multi-class ClassificationSegmentation	—Unverified

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All datasets COVID-19 CXR Dataset COVID chest X-ray Reuters-52 TII-SSRC-23 Training and validation dataset of capsule vision 2024 challenge.

Benchmark Results

#	Model	Metric	Claimed	Verified	Status
1	COVID-CXNet	Accuracy (%)	94.2	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	COVID-ResNet	F1 score	0.9	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	SVM (tficf)	Macro F1	73.9	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	Extra Trees	F1-Score	93.36	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	Multi-Model Ensemble	Mean AUC	0.99	—	Unverified