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 351–375 of 903 papers

TitleStatusHype
Graph-Based Automatic Feature Selection for Multi-Class Classification via Mean Simplified Silhouette—0
Collaborative Filtering and Multi-Label Classification with Matrix Factorization—0
Filtering Aggression from the Multilingual Social Media Feed—0
Collaborative Wideband Spectrum Sensing and Scheduling for Networked UAVs in UTM Systems—0
Effective Metaheuristic Based Classifiers for Multiclass Intrusion Detection—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
Effective Intrusion Detection for UAV Communications using Autoencoder-based Feature Extraction and Machine Learning Approach—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
Comparative Analysis of Resource-Efficient CNN Architectures for Brain Tumor Classification—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
Annotation Guidelines-Based Knowledge Augmentation: Towards Enhancing Large Language Models for Educational Text Classification—0
Comparison of Multi-Class and Binary Classification Machine Learning Models in Identifying Strong Gravitational Lenses—0
EC3: Combining Clustering and Classification for Ensemble Learning—0
Competing Ratio Loss for Discriminative Multi-class Image Classification—0
ARGUABLY at ComMA@ICON: Detection of Multilingual Aggressive, Gender Biased, and Communally Charged Tweets Using Ensemble and Fine-Tuned IndicBERT—0
Gaussian Processes on Hypergraphs—0
Generalization Analysis on Learning with a Concurrent Verifier—0
Generalization and Risk Bounds for Recurrent Neural Networks—0
Dysfluencies Seldom Come Alone -- Detection as a Multi-Label Problem—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
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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