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

Recently Added Most Hyped Most Active Needs Verification Most Verified

Showing 681–690 of 903 papers

Title	Date	Tasks	Status	Hype
Efficient Set-Valued Prediction in Multi-Class Classification	Jun 19, 2019	ClassificationGeneral Classification	CodeCode Available	1
Consistency of semi-supervised learning algorithms on graphs: Probit and one-hot methods	Jun 18, 2019	Binary ClassificationGeneral Classification	—Unverified	0
Towards Integration of Statistical Hypothesis Tests into Deep Neural Networks	Jun 15, 2019	ClassificationGeneral Classification	—Unverified	0
Non-Parametric Calibration for Classification	Jun 12, 2019	Active LearningClassification	CodeCode Available	0
Primal-Dual Block Frank-Wolfe	Jun 6, 2019	General ClassificationMulti-class Classification	CodeCode Available	0
SC-UPB at the VarDial 2019 Evaluation Campaign: Moldavian vs. Romanian Cross-Dialect Topic Identification	Jun 1, 2019	Binary ClassificationGeneral Classification	—Unverified	0
Beyond Context: A New Perspective for Word Embeddings	Jun 1, 2019	ClassificationFeature Engineering	—Unverified	0
Modeling Personal Biases in Language Use by Inducing Personalized Word Embeddings	Jun 1, 2019	Multi-class ClassificationMulti-Task Learning	—Unverified	0
On the computational complexity of the probabilistic label tree algorithms	Jun 1, 2019	Multi-class Classification	—Unverified	0
A Generalization Error Bound for Multi-class Domain Generalization	May 24, 2019	ClassificationDomain Generalization	—Unverified	0

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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