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 851–900 of 903 papers

TitleStatusHype
Faint Features Tell: Automatic Vertebrae Fracture Screening Assisted by Contrastive Learning—0
Fast Bayesian Inference for Non-Conjugate Gaussian Process Regression—0
Fast Recursive Multi-class Classification of Pairs of Text Entities for Biomedical Event Extraction—0
Fast Training of Effective Multi-class Boosting Using Coordinate Descent Optimization—0
FD-LLM: Large Language Model for Fault Diagnosis of Machines—0
Feature-aware conditional GAN for category text generation—0
Feature Incay for Representation Regularization—0
FedFACT: A Provable Framework for Controllable Group-Fairness Calibration in Federated Learning—0
Cross-System Categorization of Abnormal Traces in Microservice-Based Systems via Meta-Learning—0
Filtering Aggression from the Multilingual Social Media Feed—0
Fine-grained Generalization Analysis of Vector-valued Learning—0
Fine-tuning Large Language Models for Multigenerator, Multidomain, and Multilingual Machine-Generated Text Detection—0
FingerNet: EEG Decoding of A Fine Motor Imagery with Finger-tapping Task Based on A Deep Neural Network—0
Fixing Asymptotic Uncertainty of Bayesian Neural Networks with Infinite ReLU Features—0
An Infinite-Feature Extension for Bayesian ReLU Nets That Fixes Their Asymptotic Overconfidence—0
FLAG: Fast Label-Adaptive Aggregation for Multi-label Classification in Federated Learning—0
Flat and Nested Negation and Uncertainty Detection with PubMed BERT—0
"Flux+Mutability": A Conditional Generative Approach to One-Class Classification and Anomaly Detection—0
FOLD-SE: An Efficient Rule-based Machine Learning Algorithm with Scalable Explainability—0
FoLDTree: A ULDA-Based Decision Tree Framework for Efficient Oblique Splits and Feature Selection—0
FORML: Learning to Reweight Data for Fairness—0
Gaussian Processes on Hypergraphs—0
Generalization Analysis on Learning with a Concurrent Verifier—0
Generalization and Risk Bounds for Recurrent Neural Networks—0
Generalized Conditional Gradient for Sparse Estimation—0
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
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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