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 681–690 of 903 papers

TitleStatusHype
Multi-Label Adversarial Perturbations—0
Multi-Label Classification Method Based on Extreme Learning Machines—0
Multi-label Contrastive Predictive Coding—0
Multi-Label Gold Asymmetric Loss Correction with Single-Label Regulators—0
Multi-layer Domain Adaptation for Deep Convolutional Networks—0
Multi-level Activation for Segmentation of Hierarchically-nested Classes—0
Multi-loss ensemble deep learning for chest X-ray classification—0
Multimodal Depression Severity Prediction from medical bio-markers using Machine Learning Tools and Technologies—0
Multiobjective Optimization of Classifiers by Means of 3-D Convex Hull Based Evolutionary Algorithm—0
Multi-output Classification for Compound Fault Diagnosis in Motor under Partially Labeled Target Domain—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