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 1–10 of 903 papers

TitleStatusHype
Detecting immune cells with label-free two-photon autofluorescence and deep learning—0
SHORE: A Long-term User Lifetime Value Prediction Model in Digital Games—0
FedFACT: A Provable Framework for Controllable Group-Fairness Calibration in Federated Learning—0
GeoVision Labeler: Zero-Shot Geospatial Classification with Vision and Language ModelsCode2
Multi-output Classification using a Cross-talk Architecture for Compound Fault Diagnosis of Motors in Partially Labeled Condition—0
FinTagging: An LLM-ready Benchmark for Extracting and Structuring Financial InformationCode1
Detection of Suicidal Risk on Social Media: A Hybrid Model—0
Leveraging Cascaded Binary Classification and Multimodal Fusion for Dementia Detection through Spontaneous Speech—0
DECT-based Space-Squeeze Method for Multi-Class Classification of Metastatic Lymph Nodes in Breast CancerCode0
Self-Classification Enhancement and Correction for Weakly Supervised Object Detection—0
Show:102550
← PrevPage 1 of 91Next →
All datasetsCOVID-19 CXR DatasetCOVID chest X-rayReuters-52TII-SSRC-23Training and validation dataset of capsule vision 2024 challenge.

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1COVID-ResNetF1 score0.9—Unverified