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 861–870 of 903 papers

Title	Date	Tasks	Status
Convergence Rates of Active Learning for Maximum Likelihood Estimation	Jun 8, 2015	Active LearningBinary Classification	—Unverified
Classification with many classes: challenges and pluses	Jun 4, 2015	Classificationfeature selection	—Unverified
Active Learning for Structured Probabilistic Models With Histogram Approximation	Jun 1, 2015	Active LearningGeneral Classification	—Unverified
Deep sparse multi-task learning for feature selection in Alzheimer’s disease diagnosis	May 21, 2015	Clusteringfeature selection	—Unverified
The Best of Both Worlds: Combining Data-independent and Data-driven Approaches for Action Recognition	May 17, 2015	Action RecognitionMulti-class Classification	—Unverified
Everyone Likes Shopping! Multi-class Product Categorization for e-Commerce	May 1, 2015	Multi-class ClassificationProduct Categorization	—Unverified
Bridging Social Media via Distant Supervision	Mar 15, 2015	ClassificationGeneral Classification	—Unverified
Error-Correcting Factorization	Feb 27, 2015	Multi-class Classification	—Unverified
Discriminative training for Convolved Multiple-Output Gaussian processes	Feb 7, 2015	Activity RecognitionEmotion Recognition	—Unverified
Multiobjective Optimization of Classifiers by Means of 3-D Convex Hull Based Evolutionary Algorithm	Dec 18, 2014	Binary ClassificationClassification	—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