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 426–450 of 903 papers

TitleStatusHype
Competing Ratio Loss for Discriminative Multi-class Image Classification—0
FoLDTree: A ULDA-Based Decision Tree Framework for Efficient Oblique Splits and Feature Selection—0
Comparison of Multi-Class and Binary Classification Machine Learning Models in Identifying Strong Gravitational Lenses—0
A Recurrent CNN for Automatic Detection and Classification of Coronary Artery Plaque and Stenosis in Coronary CT Angiography—0
A High Speed Multi-label Classifier based on Extreme Learning Machines—0
Increasing Fairness via Combination with Learning Guarantees—0
Incremental user embedding modeling for personalized text classification—0
Inducing a hierarchy for multi-class classification problems—0
FOLD-SE: An Efficient Rule-based Machine Learning Algorithm with Scalable Explainability—0
"Flux+Mutability": A Conditional Generative Approach to One-Class Classification and Anomaly Detection—0
Comparison of Decision Tree Based Classification Strategies to Detect External Chemical Stimuli from Raw and Filtered Plant Electrical Response—0
Flat and Nested Negation and Uncertainty Detection with PubMed BERT—0
Insight: A Multi-Modal Diagnostic Pipeline using LLMs for Ocular Surface Disease Diagnosis—0
Comparative Analysis of Resource-Efficient CNN Architectures for Brain Tumor Classification—0
FLAG: Fast Label-Adaptive Aggregation for Multi-label Classification in Federated Learning—0
Integrating Deep Feature Extraction and Hybrid ResNet-DenseNet Model for Multi-Class Abnormality Detection in Endoscopic Images—0
Interpretable Rule-Based System for Radar-Based Gesture Sensing: Enhancing Transparency and Personalization in AI—0
An Infinite-Feature Extension for Bayesian ReLU Nets That Fixes Their Asymptotic Overconfidence—0
Comment on Is Complexity an Illusion?—0
Intrusion detection in IoT using artificial neural networks on UNSW-15 dataset—0
Fixing Asymptotic Uncertainty of Bayesian Neural Networks with Infinite ReLU Features—0
Combining Task Predictors via Enhancing Joint Predictability—0
A Rate Distortion Approach for Semi-Supervised Conditional Random Fields—0
FingerNet: EEG Decoding of A Fine Motor Imagery with Finger-tapping Task Based on A Deep Neural Network—0
Fine-tuning Large Language Models for Multigenerator, Multidomain, and Multilingual Machine-Generated Text Detection—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