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 821–830 of 903 papers

TitleStatusHype
EMG Signal Classification for Neuromuscular Disorders with Attention-Enhanced CNN—0
End-to-End Automatic Speech Recognition with Deep Mutual Learning—0
Energy-based features and bi-LSTM neural network for EEG-based music and voice classification—0
Energy-based Out-of-distribution Detection for Multi-label Classification—0
Enhanced H-Consistency Bounds—0
Understanding and Reducing the Class-Dependent Effects of Data Augmentation with A Two-Player Game Approach—0
Enhancing Multi-Class Classification of Random Forest using Random Vector Functional Neural Network and Oblique Decision Surfaces—0
Enhancing Personalized Recipe Recommendation Through Multi-Class Classification—0
Enhancing Suicide Risk Detection on Social Media through Semi-Supervised Deep Label Smoothing—0
Enhancing the Identification of Cyberbullying through Participant Roles—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