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 151–175 of 903 papers

TitleStatusHype
Multi-Class Quantum Convolutional Neural Networks—0
Hyper Evidential Deep Learning to Quantify Composite Classification UncertaintyCode1
Exploring Contrastive Learning for Long-Tailed Multi-Label Text Classification—0
Unknown Prompt, the only Lacuna: Unveiling CLIP's Potential for Open Domain GeneralizationCode1
Top-k Classification and Cardinality-Aware Prediction—0
Cross-System Categorization of Abnormal Traces in Microservice-Based Systems via Meta-Learning—0
Large Language Models for Multi-Choice Question Classification of Medical Subjects—0
Hierarchical Classification for Intrusion Detection System: Effective Design and Empirical Analysis—0
FingerNet: EEG Decoding of A Fine Motor Imagery with Finger-tapping Task Based on A Deep Neural Network—0
Neural Network Learning and Quantum Gravity—0
A Tutorial on the Pretrain-Finetune Paradigm for Natural Language Processing—0
FlowCyt: A Comparative Study of Deep Learning Approaches for Multi-Class Classification in Flow Cytometry BenchmarkingCode0
HemaGraph: Breaking Barriers in Hematologic Single Cell Classification with Graph AttentionCode0
Multi-class Temporal Logic Neural Networks—0
Understanding Self-Distillation and Partial Label Learning in Multi-Class Classification with Label Noise—0
A Convergence Analysis of Approximate Message Passing with Non-Separable Functions and Applications to Multi-Class Classification—0
BAdaCost: Multi-class Boosting with CostsCode1
PowerGraph: A power grid benchmark dataset for graph neural networks—0
Leveraging Human-Machine Interactions for Computer Vision Dataset Quality EnhancementCode0
Evaluating ML-Based Anomaly Detection Across Datasets of Varied Integrity: A Case StudyCode0
Stitching Satellites to the Edge: Pervasive and Efficient Federated LEO Satellite Learning—0
Additional Look into GAN-based Augmentation for Deep Learning COVID-19 Image Classification—0
Enumerating the k-fold configurations in multi-class classification problemsCode1
Fine-tuning Large Language Models for Multigenerator, Multidomain, and Multilingual Machine-Generated Text Detection—0
Exploring Highly Quantised Neural Networks for Intrusion Detection in Automotive CAN—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