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 181–190 of 903 papers

Title	Date	Tasks	Status	Score
Aggressive Sampling for Multi-class to Binary Reduction with Applications to Text Classification	Jan 23, 2017	Binary ClassificationClassification	CodeCode Available	5
Ensembling Uncertainty Measures to Improve Safety of Black-Box Classifiers	Aug 23, 2023	Multi-class Classification	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
Enhanced Network Embedding with Text Information	Nov 29, 2018	ClusteringMulti-class Classification	CodeCode Available	5
Competing Ratio Loss for Discriminative Multi-class Image Classification	Dec 25, 2019	Age EstimationClassification	CodeCode Available	5
Incident duration prediction using a bi-level machine learning framework with outlier removal and intra-extra joint optimisation	May 10, 2022	Binary ClassificationFeature Importance	CodeCode Available	5
A hybrid algorithm for Bayesian network structure learning with application to multi-label learning	Jun 18, 2015	General ClassificationMulti-class Classification	CodeCode Available	5
Conclusive Local Interpretation Rules for Random Forests	Apr 13, 2021	Binary ClassificationDecision Making	CodeCode Available	5
Evaluating approaches for supervised semantic labeling	Jan 29, 2018	BIG-bench Machine LearningData Integration	CodeCode Available	5
Efficient Machine Learning Ensemble Methods for Detecting Gravitational Wave Glitches in LIGO Time Series	Nov 2, 2023	Deep LearningMulti-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