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

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Showing 826–850 of 903 papers

TitleStatusHype
Understanding and Reducing the Class-Dependent Effects of Data Augmentation with A Two-Player Game Approach—0
Enhancing Multi-Class Classification of Random Forest using Random Vector Functional Neural Network and Oblique Decision Surfaces—0
Enhancing Personalized Recipe Recommendation Through Multi-Class Classification—0
Enhancing Suicide Risk Detection on Social Media through Semi-Supervised Deep Label Smoothing—0
Enhancing the Identification of Cyberbullying through Participant Roles—0
Ensemble-based Adversarial Defense Using Diversified Distance Mapping—0
Entangled Relations: Leveraging NLI and Meta-analysis to Enhance Biomedical Relation Extraction—0
E-PixelHop: An Enhanced PixelHop Method for Object Classification—0
Error-Correcting Factorization—0
Error-Correcting Output Codes with Ensemble Diversity for Robust Learning in Neural Networks—0
Estimating the Density Ratio between Distributions with High Discrepancy using Multinomial Logistic Regression—0
Evaluating Pre-Trained Models for User Feedback Analysis in Software Engineering: A Study on Classification of App-Reviews—0
Event-Event Relation Extraction using Probabilistic Box Embedding—0
Everyone Likes Shopping! Multi-class Product Categorization for e-Commerce—0
Evolutionary Simplicial Learning as a Generative and Compact Sparse Framework for Classification—0
Explainable Multi-class Classification of Medical Data—0
Explainable Multi-class Classification of the CAMH COVID-19 Mental Health Data—0
Explicit Facial Expression Transfer via Fine-Grained Representations—0
A Framework for Behavioral Biometric Authentication using Deep Metric Learning on Mobile Devices—0
Exploring Contrastive Learning for Long-Tailed Multi-Label Text Classification—0
Exploring Highly Quantised Neural Networks for Intrusion Detection in Automotive CAN—0
Exploring the Uncertainty Properties of Neural Networks’ Implicit Priors in the Infinite-Width Limit—0
Exploring the Uncertainty Properties of Neural Networks' Implicit Priors in the Infinite-Width Limit—0
Eye Disease Classification Using Deep Learning Techniques—0
Factorizable Joint Shift in Multinomial Classification—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
#ModelMetricClaimedVerifiedStatus
1COVID-ResNetF1 score0.9—Unverified
#ModelMetricClaimedVerifiedStatus
1SVM (tficf)Macro F173.9—Unverified
#ModelMetricClaimedVerifiedStatus
1Extra TreesF1-Score93.36—Unverified
#ModelMetricClaimedVerifiedStatus
1Multi-Model EnsembleMean AUC0.99—Unverified