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 826–850 of 903 papers

TitleStatusHype
Performance of GAN-based augmentation for deep learning COVID-19 image classificationCode0
The EarlyBIRD Catches the Bug: On Exploiting Early Layers of Encoder Models for More Efficient Code ClassificationCode0
Improving Bias Mitigation through Bias Experts in Natural Language UnderstandingCode0
Scalable Gaussian Process Classification with Additive Noise for Various LikelihoodsCode0
Improving Generalizability of Kolmogorov-Arnold Networks via Error-Correcting Output CodesCode0
Transfer learning approach to Classify the X-ray image that corresponds to corona disease Using ResNet50 pretrained by ChexNetCode0
Batch Selection for Multi-Label Classification Guided by Uncertainty and Dynamic Label CorrelationsCode0
SciPrompt: Knowledge-augmented Prompting for Fine-grained Categorization of Scientific TopicsCode0
Improving the repeatability of deep learning models with Monte Carlo dropoutCode0
Deep localization of protein structures in fluorescence microscopy imagesCode0
Incident duration prediction using a bi-level machine learning framework with outlier removal and intra-extra joint optimisationCode0
InClass Nets: Independent Classifier Networks for Nonparametric Estimation of Conditional Independence Mixture Models and Unsupervised ClassificationCode0
Understanding Deep Representation Learning via Layerwise Feature Compression and DiscriminationCode0
A Novel Multi-Stage Approach for Hierarchical Intrusion DetectionCode0
Combating Hostility: Covid-19 Fake News and Hostile Post Detection in Social MediaCode0
Segmentation of Anatomical Layers and Artifacts in Intravascular Polarization Sensitive Optical Coherence Tomography Using Attending Physician and Boundary Cardinality LossesCode0
Deep Distribution RegressionCode0
Information-theoretic Classification Accuracy: A Criterion that Guides Data-driven Combination of Ambiguous Outcome Labels in Multi-class ClassificationCode0
Active Learning from Positive and Unlabeled DataCode0
Injecting Hierarchical Biological Priors into Graph Neural Networks for Flow Cytometry PredictionCode0
MultiGuard: Provably Robust Multi-label Classification against Adversarial ExamplesCode0
Instance-based Label Smoothing For Better Calibrated Classification NetworksCode0
Deep brain state classification of MEG dataCode0
Self-supervised Mean Teacher for Semi-supervised Chest X-ray ClassificationCode0
Label Distributionally Robust Losses for Multi-class Classification: Consistency, Robustness and AdaptivityCode0
Show:102550
← PrevPage 34 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