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 711–720 of 903 papers

TitleStatusHype
Big Models for Big Data using Multi objective averaged one dependence estimators—0
Binary and Multi-Class Intrusion Detection in IoT Using Standalone and Hybrid Machine and Deep Learning Models—0
Binary output layer of feedforward neural networks for solving multi-class classification problems—0
Binary Stochastic Representations for Large Multi-class Classification—0
Biomarker based Cancer Classification using an Ensemble with Pre-trained Models—0
Biomedical Event Extraction by Multi-class Classification of Pairs of Text Entities—0
Breaking the Token Barrier: Chunking and Convolution for Efficient Long Text Classification with BERT—0
Bridging Social Media via Distant Supervision—0
Building an Interpretable Recommender via Loss-Preserving Transformation—0
Candidates vs. Noises Estimation for Large Multi-Class Classification Problem—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
#ModelMetricClaimedVerifiedStatus
1SVM (tficf)Macro F173.9—Unverified
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1Extra TreesF1-Score93.36—Unverified
#ModelMetricClaimedVerifiedStatus
1Multi-Model EnsembleMean AUC0.99—Unverified