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 876–900 of 903 papers

TitleStatusHype
Generative Adversarial Networks based on Mixed-Attentions for Citation Intent Classification in Scientific Publications—0
Generative-Discriminative Variational Model for Visual Recognition—0
Genetic Column Generation for Computing Lower Bounds for Adversarial Classification—0
Geolocation with Attention-Based Multitask Learning Models—0
GJG@TamilNLP-ACL2022: Emotion Analysis and Classification in Tamil using Transformers—0
GJG@TamilNLP-ACL2022: Using Transformers for Abusive Comment Classification in Tamil—0
Global Capacity Measures for Deep ReLU Networks via Path Sampling—0
GMM-Based Synthetic Samples for Classification of Hyperspectral Images With Limited Training Data—0
Granular Ball K-Class Twin Support Vector Classifier—0
Graph-Based Automatic Feature Selection for Multi-Class Classification via Mean Simplified Silhouette—0
Graph-based Extreme Feature Selection for Multi-class Classification Tasks—0
Graph Convolutional Networks for Classification with a Structured Label Space—0
Graph Residual based Method for Molecular Property Prediction—0
GraphX^NET- Chest X-Ray Classification Under Extreme Minimal Supervision—0
Harnessing Deep Learning and Satellite Imagery for Post-Buyout Land Cover Mapping—0
Hashtag Recommendation Using End-To-End Memory Networks with Hierarchical Attention—0
Hierarchical Classification for Intrusion Detection System: Effective Design and Empirical Analysis—0
Hierarchical Clustering Beyond the Worst-Case—0
Hierarchical Deep Learning with Generative Adversarial Network for Automatic Cardiac Diagnosis from ECG Signals—0
Hierarchical Feature Hashing for Fast Dimensionality Reduction—0
Hierarchical Neyman-Pearson Classification for Prioritizing Severe Disease Categories in COVID-19 Patient Data—0
HMIC: Hierarchical Medical Image Classification, A Deep Learning Approach—0
How can we generalise learning distributed representations of graphs?—0
How many faces can be recognized? Performance extrapolation for multi-class classification—0
How optimal transport can tackle gender biases in multi-class neural-network classifiers for job recommendations?—0
Show:102550
← PrevPage 36 of 37Next →
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