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 621–630 of 903 papers

Title	Date	Tasks	Status
On Using Transfer Learning For Plant Disease Detection	May 14, 2020	ClassificationGeneral Classification	—Unverified
Automated diagnosis of COVID-19 with limited posteroanterior chest X-ray images using fine-tuned deep neural networks	Apr 23, 2020	Binary ClassificationComputed Tomography (CT)	CodeCode Available
Federated Learning with Only Positive Labels	Apr 21, 2020	Federated LearningMulti-class Classification	CodeCode Available
Conditional-UNet: A Condition-aware Deep Model for Coherent Human Activity Recognition From Wearables	Apr 15, 2020	Activity RecognitionGesture Recognition	—Unverified
COVID-ResNet: A Deep Learning Framework for Screening of COVID19 from Radiographs	Mar 31, 2020	Multi-class Classification	CodeCode Available
Adversarial Multi-Binary Neural Network for Multi-class Classification	Mar 25, 2020	ClassificationGeneral Classification	—Unverified
Multi-Class classification of vulnerabilities in Smart Contracts using AWD-LSTM, with pre-trained encoder inspired from natural language processing	Mar 21, 2020	Binary ClassificationGeneral Classification	—Unverified
Diagnosis of Diabetic Retinopathy in Ethiopia: Before the Deep Learning based Automation	Mar 20, 2020	Binary ClassificationClassification	—Unverified
Domain Adaptation with Conditional Distribution Matching and Generalized Label Shift	Mar 10, 2020	Domain AdaptationMulti-class Classification	—Unverified
On the Learning Property of Logistic and Softmax Losses for Deep Neural Networks	Mar 4, 2020	Binary ClassificationClassification	CodeCode Available

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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