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 581–590 of 903 papers

TitleStatusHype
Top-k Classification and Cardinality-Aware Prediction—0
Toward an Efficient Multi-class Classification in an Open Universe—0
Toward Optimal Feature Selection in Naive Bayes for Text Categorization—0
Towards Adversarial Realism and Robust Learning for IoT Intrusion Detection and Classification—0
Towards Integration of Statistical Hypothesis Tests into Deep Neural Networks—0
Towards Speaker Age Estimation with Label Distribution Learning—0
Training Multi-Layer Binary Neural Networks With Local Binary Error Signals—0
Transformer-Based Speech Synthesizer Attribution in an Open Set Scenario—0
Transformer Models for Acute Brain Dysfunction Prediction—0
Transparency Promotion with Model-Agnostic Linear Competitors—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
#ModelMetricClaimedVerifiedStatus
1Multi-Model EnsembleMean AUC0.99—Unverified