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 401–425 of 903 papers

TitleStatusHype
Computer Aided Detection of Anemia-like Pallor—0
Hierarchical Deep Learning with Generative Adversarial Network for Automatic Cardiac Diagnosis from ECG Signals—0
Hierarchical Feature Hashing for Fast Dimensionality Reduction—0
Hierarchical Neyman-Pearson Classification for Prioritizing Severe Disease Categories in COVID-19 Patient Data—0
HMIC: Hierarchical Medical Image Classification, A Deep Learning Approach—0
Armijo Line-search Can Make (Stochastic) Gradient Descent Provably Faster—0
Generalization and Risk Bounds for Recurrent Neural Networks—0
How many faces can be recognized? Performance extrapolation for multi-class classification—0
How optimal transport can tackle gender biases in multi-class neural-network classifiers for job recommendations?—0
Generalization Analysis on Learning with a Concurrent Verifier—0
Hybrid Machine Learning Model for Detecting Bangla Smishing Text Using BERT and Character-Level CNN—0
ComplAI: Theory of A Unified Framework for Multi-factor Assessment of Black-Box Supervised Machine Learning Models—0
Identifying Domain Independent Update Intents in Task Based Dialogs—0
Image Classification using Combination of Topological Features and Neural Networks—0
Gaussian Processes on Hypergraphs—0
ARGUABLY at ComMA@ICON: Detection of Multilingual Aggressive, Gender Biased, and Communally Charged Tweets Using Ensemble and Fine-Tuned IndicBERT—0
FORML: Learning to Reweight Data for Fairness—0
Improved Generalization Bounds for Adversarially Robust Learning—0
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
Improving Low-Resource Named Entity Recognition using Joint Sentence and Token Labeling—0
Improving Primate Sounds Classification using Binary Presorting for Deep Learning—0
A High Speed Multi-label Classifier based on Extreme Learning Machines—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