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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 631–640 of 903 papers

TitleStatusHype
Comparison of Multi-Class and Binary Classification Machine Learning Models in Identifying Strong Gravitational Lenses—0
Imbalance Learning for Variable Star ClassificationCode0
On the generalization of bayesian deep nets for multi-class classification—0
Optimistic bounds for multi-output prediction—0
The Utility of General Domain Transfer Learning for Medical Language Tasks—0
Multi-Class Classification from Noisy-Similarity-Labeled Data—0
Improving automated segmentation of radio shows with audio embeddings—0
A Non-Intrusive Correction Algorithm for Classification Problems with Corrupted Data—0
Certified Robustness to Label-Flipping Attacks via Randomized Smoothing—0
Antifragility Predicts the Robustness and Evolvability of Biological Networks through Multi-class Classification with a Convolutional Neural Network—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