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 301–325 of 903 papers

TitleStatusHype
Okapi: Generalising Better by Making Statistical Matches MatchCode0
Generalized Consistent Multi-Class Classification with Rejection to be Compatible with Arbitrary LossesCode0
Projection Valued Measure-based Quantum Machine Learning for Multi-Class Classification—0
Dysfluencies Seldom Come Alone -- Detection as a Multi-Label Problem—0
One-Class Risk Estimation for One-Class Hyperspectral Image ClassificationCode1
An Attention-based Long Short-Term Memory Framework for Detection of Bitcoin Scams—0
Proximal Mean Field Learning in Shallow Neural NetworksCode0
An Effective Approach for Multi-label Classification with Missing Labels—0
Calibration tests beyond classificationCode0
Hierarchical Deep Learning with Generative Adversarial Network for Automatic Cardiac Diagnosis from ECG Signals—0
Systematic Evaluation of Predictive FairnessCode0
Explainable Causal Analysis of Mental Health on Social Media DataCode1
Transformer-Based Speech Synthesizer Attribution in an Open Set Scenario—0
What Makes Graph Neural Networks Miscalibrated?Code1
Generalization Analysis on Learning with a Concurrent Verifier—0
Effective Metaheuristic Based Classifiers for Multiclass Intrusion Detection—0
MultiGuard: Provably Robust Multi-label Classification against Adversarial ExamplesCode0
Is Encoder-Decoder Transformer the Shiny Hammer?—0
UB Health Miners@SMM4H’22: Exploring Pre-processing Techniques To Classify Tweets Using Transformer Based Pipelines.—0
Hierarchical Neyman-Pearson Classification for Prioritizing Severe Disease Categories in COVID-19 Patient Data—0
Class-Imbalanced Complementary-Label Learning via Weighted Loss—0
Source detection via multi-label classificationCode0
CAMRI Loss: Improving Recall of a Specific Class without Sacrificing AccuracyCode0
MulBot: Unsupervised Bot Detection Based on Multivariate Time Series—0
Neural Collapse with Normalized Features: A Geometric Analysis over the Riemannian ManifoldCode0
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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