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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 331–340 of 903 papers

TitleStatusHype
Faint Features Tell: Automatic Vertebrae Fracture Screening Assisted by Contrastive Learning—0
Semi-Supervised Anomaly Detection Based on Quadratic Multiform Separation—0
FOLD-SE: An Efficient Rule-based Machine Learning Algorithm with Scalable Explainability—0
TagRec++: Hierarchical Label Aware Attention Network for Question CategorizationCode0
CIGAN: A Python Package for Handling Class Imbalance using Generative Adversarial NetworksCode1
Retrieval of surgical phase transitions using reinforcement learning—0
Factorizable Joint Shift in Multinomial Classification—0
Detecting Spam Reviews on Vietnamese E-commerce WebsitesCode1
MAPIE: an open-source library for distribution-free uncertainty quantificationCode3
A novel Deep Learning approach for one-step Conformal Prediction approximationCode0
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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