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 661–670 of 903 papers

TitleStatusHype
Reconsidering Analytical Variational Bounds for Output Layers of Deep Networks—0
Multi-classifier prediction of knee osteoarthritis progression from incomplete imbalanced longitudinal data—0
A Quest for Structure: Jointly Learning the Graph Structure and Semi-Supervised ClassificationCode0
How can we generalise learning distributed representations of graphs?—0
Learning with Protection: Rejection of Suspicious Samples under Adversarial Environment—0
Scalable Gaussian Process Classification with Additive Noise for Various LikelihoodsCode0
Semi-supervised Vector-valued Learning: Improved Bounds and AlgorithmsCode0
Road Mapping In LiDAR Images Using A Joint-Task Dense Dilated Convolutions Merging Network—0
Student Performance Prediction with Optimum Multilabel Ensemble Model—0
Explicit Facial Expression Transfer via Fine-Grained Representations—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