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 701–710 of 903 papers

TitleStatusHype
Label Mapping Neural Networks with Response Consolidation for Class Incremental Learning—0
TRk-CNN: Transferable Ranking-CNN for image classification of glaucoma, glaucoma suspect, and normal eyes—0
Prediction and outlier detection in classification problems—0
Multiclass Language Identification using Deep Learning on Spectral Images of Audio Signals—0
Ocular Diseases Diagnosis in Fundus Images using a Deep Learning: Approaches, tools and Performance evaluation—0
Question Relatedness on Stack Overflow: The Task, Dataset, and Corpus-inspired Models—0
Outlier Detection from Image Data—0
Weakly and Semi Supervised Detection in Medical Imaging via Deep Dual Branch Net—0
Classification and Detection in Mammograms with Weak Supervision via Dual Branch Deep Neural Net—0
ML-KFHE: Multi-label ensemble classification algorithm exploiting sensor fusion properties of the Kalman filter—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
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1COVID-ResNetF1 score0.9—Unverified
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1SVM (tficf)Macro F173.9—Unverified
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1Extra TreesF1-Score93.36—Unverified
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1Multi-Model EnsembleMean AUC0.99—Unverified