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 271–280 of 903 papers

Title	Date	Tasks	Status	Score
Ensembling Uncertainty Measures to Improve Safety of Black-Box Classifiers	Aug 23, 2023	Multi-class Classification	CodeCode Available	5
Efficient Deep Learning for Stereo Matching	Jun 1, 2016	Deep LearningGeneral Classification	CodeCode Available	5
A novel Deep Learning approach for one-step Conformal Prediction approximation	Jul 25, 2022	Conformal PredictionDeep Learning	CodeCode Available	5
DS-MLR: Exploiting Double Separability for Scaling up Distributed Multinomial Logistic Regression	Apr 16, 2016	BlockingMulti-class Classification	CodeCode Available	5
Beyond Adult and COMPAS: Fairness in Multi-Class Prediction	Jun 15, 2022	FairnessMulti-class Classification	CodeCode Available	5
Characterizing Data Point Vulnerability via Average-Case Robustness	Jul 26, 2023	Adversarial RobustnessMulti-class Classification	CodeCode Available	5
Federated Learning with Only Positive Labels	Apr 21, 2020	Federated LearningMulti-class Classification	CodeCode Available	5
Few-Shot Transfer Learning to improve Chest X-Ray pathology detection using limited triplets	Apr 16, 2022	DiagnosticFew-Shot Learning	CodeCode Available	5
FlowCyt: A Comparative Study of Deep Learning Approaches for Multi-Class Classification in Flow Cytometry Benchmarking	Feb 28, 2024	BenchmarkingInductive Learning	CodeCode Available	5
Diagnosis of COVID-19 using chest X-ray images based on modified DarkCovidNet model	Mar 9, 2022	Binary ClassificationCOVID-19 Diagnosis	CodeCode Available	5

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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