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 71–80 of 903 papers

Title	Date	Tasks	Status	Hype
Robustness of Accuracy Metric and its Inspirations in Learning with Noisy Labels	Dec 8, 2020	Learning with noisy labelsModel Selection	CodeCode Available	1
PMLB v1.0: An open source dataset collection for benchmarking machine learning methods	Nov 30, 2020	BenchmarkingBIG-bench Machine Learning	CodeCode Available	1
A Deep Neural Network for SSVEP-based Brain-Computer Interfaces	Nov 17, 2020	Brain Computer InterfaceEEG	CodeCode Available	1
KINNEWS and KIRNEWS: Benchmarking Cross-Lingual Text Classification for Kinyarwanda and Kirundi	Oct 23, 2020	ArticlesBenchmarking	CodeCode Available	1
PANDA: Adapting Pretrained Features for Anomaly Detection and Segmentation	Oct 12, 2020	Anomaly DetectionAnomaly Segmentation	CodeCode Available	1
A Fully Hyperbolic Neural Model for Hierarchical Multi-Class Classification	Oct 5, 2020	ClassificationEntity Typing	CodeCode Available	1
Self-Supervised Meta-Learning for Few-Shot Natural Language Classification Tasks	Sep 17, 2020	General ClassificationLanguage Modeling	CodeCode Available	1
Spatio-Temporal EEG Representation Learning on Riemannian Manifold and Euclidean Space	Aug 19, 2020	Binary ClassificationDecision Making	CodeCode Available	1
Curriculum learning for improved femur fracture classification: scheduling data with prior knowledge and uncertainty	Jul 31, 2020	ClassificationGeneral Classification	CodeCode Available	1
Online probabilistic label trees	Jul 8, 2020	Few-Shot LearningMulti-class Classification	CodeCode Available	1

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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