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 261–270 of 903 papers

Title	Date	Tasks	Status	Score
Concise Explanations of Neural Networks using Adversarial Training	Oct 15, 2018	Multi-class Classification	CodeCode Available	5
Evaluating approaches for supervised semantic labeling	Jan 29, 2018	BIG-bench Machine LearningData Integration	CodeCode Available	5
Domain Adaptation with Cauchy-Schwarz Divergence	May 30, 2024	Domain AdaptationMulti-class Classification	CodeCode Available	5
DOLDA - a regularized supervised topic model for high-dimensional multi-class regression	Jan 31, 2016	General ClassificationMulti-class Classification	CodeCode Available	5
Ensembling Uncertainty Measures to Improve Safety of Black-Box Classifiers	Aug 23, 2023	Multi-class Classification	CodeCode Available	5
Divide and Conquer: An Ensemble Approach for Hostile Post Detection in Hindi	Jan 20, 2021	Multi-class ClassificationMulti-Label Text Classification	CodeCode Available	5
An Exploration of Softmax Alternatives Belonging to the Spherical Loss Family	Nov 16, 2015	Language ModelingLanguage Modelling	CodeCode Available	5
Every Untrue Label is Untrue in its Own Way: Controlling Error Type with the Log Bilinear Loss	Apr 20, 2017	Multi-class Classification	CodeCode Available	5
Distance Guided Generative Adversarial Network for Explainable Binary Classifications	Dec 29, 2023	Binary ClassificationClassification	CodeCode Available	5
Emulating Spatio-Temporal Realizations of Three-Dimensional Isotropic Turbulence via Deep Sequence Learning Models	Dec 7, 2021	Dimensionality ReductionMulti-class Classification	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