SOTAVerified

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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 351–375 of 903 papers

TitleStatusHype
A Multi-In and Multi-Out Dendritic Neuron Model and its Optimization—0
Additional Look into GAN-based Augmentation for Deep Learning COVID-19 Image Classification—0
A Comparative Analysis of Machine Learning Techniques for IoT Intrusion Detection—0
Counterfactual Explanations for Predictive Business Process Monitoring—0
Correlation-based construction of neighborhood and edge features—0
ATESA-BÆRT: A Heterogeneous Ensemble Learning Model for Aspect-Based Sentiment Analysis—0
Convolutional Neural Networks in Multi-Class Classification of Medical Data—0
Convergence rates of sub-sampled Newton methods—0
A Survey on Open Set Recognition—0
Convergence Rates of Active Learning for Maximum Likelihood Estimation—0
Convergence of Uncertainty Sampling for Active Learning—0
A Stutter Seldom Comes Alone -- Cross-Corpus Stuttering Detection as a Multi-label Problem—0
Contrastive Learning for Fair Representations—0
Aspect category learning and sentimental analysis using weakly supervised learning—0
A multi-class structured dictionary learning method using discriminant atom selection—0
Constrained Multi-Layer Contrastive Learning for Implicit Discourse Relationship Recognition—0
Consistency of semi-supervised learning algorithms on graphs: Probit and one-hot methods—0
A simple technique for improving multi-class classification with neural networks—0
Confidence Prediction for Lexicon-Free OCR—0
Confidence Calibration for Domain Generalization under Covariate Shift—0
A scalable stage-wise approach to large-margin multi-class loss based boosting—0
Artificial intelligence supported anemia control system (AISACS) to prevent anemia in maintenance hemodialysis patients—0
A Data-Driven Pool Strategy for Price-Makers Under Imperfect Information—0
Efficient Malicious UAV Detection Using Autoencoder-TSMamba Integration—0
1-D Residual Convolutional Neural Network coupled with Data Augmentation and Regularization for the ICPHM 2023 Data Challenge—0
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All datasetsCOVID-19 CXR DatasetCOVID chest X-rayReuters-52TII-SSRC-23Training and validation dataset of capsule vision 2024 challenge.

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1COVID-CXNetAccuracy (%)94.2—Unverified
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1COVID-ResNetF1 score0.9—Unverified
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1SVM (tficf)Macro F173.9—Unverified
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1Extra TreesF1-Score93.36—Unverified
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1Multi-Model EnsembleMean AUC0.99—Unverified