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 301–310 of 903 papers

Title	Date	Tasks	Status	Hype
Okapi: Generalising Better by Making Statistical Matches Match	Nov 7, 2022	BenchmarkingBinary Classification	CodeCode Available	0
Generalized Consistent Multi-Class Classification with Rejection to be Compatible with Arbitrary Losses	Nov 1, 2022	ClassificationMulti-class Classification	CodeCode Available	0
Projection Valued Measure-based Quantum Machine Learning for Multi-Class Classification	Oct 30, 2022	Multi-class ClassificationQuantum Machine Learning	—Unverified	0
Dysfluencies Seldom Come Alone -- Detection as a Multi-Label Problem	Oct 28, 2022	Multi-class Classificationspeech-recognition	—Unverified	0
One-Class Risk Estimation for One-Class Hyperspectral Image Classification	Oct 27, 2022	ClassificationDeep Learning	CodeCode Available	1
An Attention-based Long Short-Term Memory Framework for Detection of Bitcoin Scams	Oct 26, 2022	Multi-class Classification	—Unverified	0
Proximal Mean Field Learning in Shallow Neural Networks	Oct 25, 2022	Multi-class Classification	CodeCode Available	0
An Effective Approach for Multi-label Classification with Missing Labels	Oct 24, 2022	ClassificationMissing Labels	—Unverified	0
Calibration tests beyond classification	Oct 21, 2022	ClassificationDecision Making	CodeCode Available	0
Hierarchical Deep Learning with Generative Adversarial Network for Automatic Cardiac Diagnosis from ECG Signals	Oct 19, 2022	Anomaly DetectionGenerative Adversarial Network	—Unverified	0

Show:10 25 50

← PrevPage 31 of 91Next →

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